In this paper , we present lesion detection schemes in dermoscopy images on mobile platforms . The systems are based on density based clustering ( DBSCAN ) and fuzzy c-means ( FCM ) clustering and developed for Windows Phone and Android environments . We tested the systems on dermoscopy images and ROIs are successfully extracted . The problem occurs due to dispatching taxis is not in line with the taxi demand in different functional regions during different time periods . The method proposed in this paper clustered taxi Origin-Destination data points of the functional region with the improved DB-SCAN algorithm and yielded the areas of interest with the statistical method named Chi-square distribution . The accuracy and credibility of the method was evaluated through two indicators called coverage and hit ratio and a comparison analysis on situations of different regions . Noninvasive BCI techniques play a major role for this purpose . In this paper , we have proposed three algorithms of cursor movement using three well-known clustering methods ( Minimum distance , DB-Scan and Gaussian Mixture Model ) . These proposed techniques are generally tested on motor imagery EEG data . The second makes use of transfer functions as a mechanism to generate binary versions . The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function ( TF ) and random operators . The Alisan Creek watershed in central Taiwan was one of serious hazard areas . This study proposed ( 1 ) two-layer SOM-based classifier coupled with pre- and post-typhoon SPOT satellite images to extract accurate landslide , ( 2 ) the concept of landslide slope unit ( LSU ) derived from automated watershed modeling delineation to calculate spatial characteristics of the landslide and ( 3 ) DBSCAN-based model to analyze the spatial correlations at landslides to assess the priority of landslide treatment sites . The evaluated result shows that the landslide area soon after the typhoon is 619.91 ha ( Kappa coefficient 0.9766 ) . The aim of this paper is to propose a method to cluster GPS data corresponding to driving destinations . A new DBSCAN-based algorithm is proposed to group stationary GPS traces , collected prior to end of trips , into destination clusters . While the original DBSCAN clustering algorithm uses a global threshold as a closeness measure in data space , we develop a method to set local thresholds values for data points ; this is important because the GPS data proximity strongly depends on the density of the street grid around each point . During the mapping procedure , the partitioned segments are mapped into the corresponding cells , and the mapping relationship between the segments and the cells are stored . In the clustering phase , adopting the DBSCAN-based method , the segments in the cells are clustered on the basis of the calibrated values of parameters from the mapping procedure . The extensive experiments indicate that although the results of the adaptive parameter calibration are not optimal , in most cases , the difference between the adaptive calibration and the optimal is less than 5 % , while the run time of clustering can reduce about 95 % , compared with the TRACLUS algorithm . This paper proposes DBSCAN-SI that is an algorithm for clustering with influences of spatial objects . DBSCAN-SI that is extended from existing DBSCAN and DBSCAN-W converts from non-spatial properties to the influences of spatial objects during the spatial clustering . It increases probability of inclusion to the cluster according to the higher the influences that is affected by the properties used in clustering and executes the clustering not only respect the spatial distances , but also volume of influences . Abstract Based on the DBSCAN analysis , kernel density analysis and word cloud analysis , this paper analyzed the composition types and spatio-temporal evolution characteristics of catering points and catering clusters form macro , meso and micro dimensions within 5th ring road in Beijing with the caterings POI data in 2012 , 2014 and 2016 . The conclusions are as follows : first : ( 1 ) The number of total catering POI increased by about 6 times , and the center of the whole data shows a tendency to move from west to southeast . Average silhouette width score is used here to compare the quality of clusters formed by both algorithms . By testing 2DBSCAN-LOD on different artificial datasets , it is found that the average silhouette width score of clusters formed by DBSCAN-LOD is higher than that of the clusters formed by DBSCAN . Density-based spatial clustering of applications with noise ( DBSCAN ) is the most commonly used density-based clustering algorithm , where it can discover multiple clusters with arbitrary shapes . DBSCAN works properly when the input data type is homogeneous , but the DBSCAN 's approach may not be sufficient when the input dataset has textual heterogeneity ( e.g. , when we intend to find clusters from geo-tagged posts on social media relevant to a certain point-of-interest ( POI ) ) , thus leading to poor performance . In this paper , we present DBSTexC , a new density-based clustering algorithm using spatio -- textual information on Twitter . This may result in the system being abandoned , or worse , waste the defender ’s time investigating false alarms . While our attacks generalize to all DBSCANbased tools , we focus our evaluation on AnDarwin , a tool designed to detect plagiarized Android apps . We show that an adversary can merge arbitrary clusters by connecting them with “ bridges ” , that even a small number of merges can greatly degrade clustering performance , and that the defender has limited recourse when relying solely on DBSCAN . It is divided into the following steps : 1 ) partitioning the raw data based on a random sample , 2 ) computing local DBSCAN algorithms in parallel , 3 ) merging the data partitions based on the centroid . Compared with the traditional DBSCAN algorithm , the experimental result shows the proposed S_DBSCAN algorithm provides better operating efficiency and scalability . Radio Frequency Identification RFID system is a low-cost contactless automatic identification technology ; and barcode as a traditional technology is now broadly replaced by RFID systems to make objects more manageable in supply chains and other enterprises . This technology is operational in open wireless communication spaces whereby its transmission signals can be easily accessed resulting in security problems . As the radio frequency identification ( RFID ) technology continues to evolve and mature , RFID tags can be implemented in a wide range of applications . Due to the shared wireless medium between the RFID reader and the RFID tag , adversaries can launch various attacks on the RFID system . In this paper , we propose a novel radio-frequency identification (RFID)-based methodology that can estimate the position and orientation of an object . Unlike other RFID-based localization systems , the proposed method employs a single nonsteered stationary antenna and three passive tags mounted on each object . Indoor/outdoor localization has gained importance as it has the potential to improve various processes related to the resource management of construction projects and to deliver personalized and location-based services ( LBS ) . Radio Frequency Identification ( RFID ) based systems , have been widely used in different applications in construction and maintenance . This paper investigates the usage of active RFID technology for the localization of movable objects ( e.g. material , equipment , tools , and assets ) equipped with RFID tags using handheld readers . The radio frequency identification system ( RFID ) is becoming one of the most popular system in wireless technologies . The UHF RFID tag emulator is a part of RFID testing tools . Abstract The present article is devoted to the issue of improvement of utilization efficiency of modern information technologies in order to enhance the quality and safety of healthcare delivery . The introduction of Real-time Locating Systems ( RTLS ) , in particular Radio Frequency Identification System ( RFID ) , applied in conjunction with intelligent data processing system can reduce the severity of the consequences of event risks implementation , as well as avoidance of a number of such consequences . The authors of the present article suggest using mathematical tools of fuzzy logic theory for event analysis in online mode and recommendation formation on their basis for timely management decision-taking . A human object tracking algorithm fused with depth information , color information and forecast information is presented in this paper , which enables follow-robot to track motion human object robustly . Firstly , Camshift algorithm is expended into three-dimensional space by color information and depth information . Secondly , the interference from complex background is eliminated from the projection drawing by depth image of motion human object in three-dimensional space . This paper presents the results of a factorial experiment performed to determine the effect of illumination compensation methods with histogram back projection to be used for object tracking algorithm continuous adaptive mean-shift ( Camshift ) . Since Camshift tracking can be used for distance approximation of an object , a precise tracker algorithm is required . In order to achieve intelligent feedback tracking of moving target , an embedded system for recognition and tracking of moving target based on the DSP chip and Camshift algorithm was designed . The system uses frame difference method to recognize the moving target and Camshift algorithm to track the moving target . Condensation algorithm is used for estimating the states of the targets and CAMShift is used for data association . We have used ConDensation algorithm for multiple target tracking in single camera FoV , where the data association is done using the search window approximated by the CAMshift filter for each target . It takes care of errors in tracking which may arise due to the splitting of targets , a common phenomena in detection of articulated objects . There are several non-invasive and computationally inexpensive techniques for face tracking that draw upon algorithms from computer vision . Of them , Bradski 's CAMSHIFT algorithm is appealing because it requires minimal training . These techniques are particularly attractive in light of the growing installed base of fast desktop computers and cheap , low-end desktop digital video cameras . This paper propose a real-time combined method of Camshift [ 1 ] algorithm and Haar-like feature detection [ 2 ] for tracking and recognizing hand gesture in images acquired by a possibly moving camera . A Haar-like classifier is used during the initializing of the system to acquire the user 's hand color . Finally , it makes comparative experiments between the improved algorithms and general Camshift . The experimental results show that , combined with the background difference method and the continuous Camshift algorithm for tracking of moving objects , to some extent it can meet the real-time and stability requirements . In this work , we explore video processing for driving assistance system . An object ’s detection and tracking system based on the Faster R-CNN and Camshift algorithm is proposed , and Kalman filtering algorithm is used to predict the position of objects . Our system differs with other object-tracking algorithms based on deep learning , which directly detects each frame and has no tracking part , such as YOLO and SSD . Multi-cue-based Camshift algorithm is presented for object tracking in complex scenes . Because color-based Camshift algorithm suffers from objects with color clutter in backgrounds , this paper integrates motion cue with color cue to extend its application areas . To realize the accurate recognition of manual alphabets in the sign language video , this paper presentes an improved algorithm based on DI _ CamShift ( depth image CamShift ) and SLVW ( sign language visual word ) multi-features combine . First , the video and depth image information of sign language gestures are obtained by usesing Kinect . Camshift could accurately follow the moving target under the simple background , but at a complicated one it was very difficult for Camshift to accurately track a target . Predicted Camshift was used to track the real-time moving target in this paper . It takes the centroid of the Camshift as the observation signal , and adopts the Kalman filter to predict and correct the targetpsilas centroid . In this paper we describe a Camshift implementation on mobile robotic system for tracking and pursuing a moving person with a monocular camera . Camshift algorithm uses color distribution information to track moving object . This paper presents an efficient combination of two well-known tracking algorithms , Tracking-Learning-Detection ( TLD ) and Compressive Tracking ( CT ) to devise an algorithm which takes advantages of both and outperforms them on their short-ends by virtue of other . TLD fails in cases including full out-of-plane rotation , fast motion and articulated object tracking . To solve the problems of tracking errors such as target missing that emerged in compressive tracking ( CT ) algorithm due to factors such as pose variation , illumination change , and occlusion , a novel tracking algorithm combined angular point matching with compressive tracking ( APMCCT ) was proposed . A sparse measurement matrix was adopted to extract the Haar-like features . Building on our previous video-to-text ( V2 T ) Fusion2014 paper , we extend the method for evaluation of eight tracking methods compared for extraction of semantic information including target number , category , attribute , and direction . Using the CMUSphinx speech-to-text system for semantic parsing of user call-outs , preliminary results show the integration of video tracking and text analysis is better with the compressive tracker ( CT ) and the Tracking-Learning-Detection ( TLD ) method . The feature analysis of the CT and TLD demonstrate the ability to associate user call-out text-based semantic descriptors with video exploitation . In order to solve the compressive tracking algorithm can not solve the change of scale and rotation in object tracking , an adaptive compressive tracking algorithm based on SIFT features is proposed . Firstly , we using SIFT feature extracted the scale-space extremes points of the object model and the tracking frame , Then the scale and rotation angle of the object being tracked were calculated by using the scale and angle information which was provided by the feature points correctly matched between model and the frame , we calculate the main-orientation information of the object and the scale of object according those refined scale-space extremes point , then using compressive sense tracking algorithm to locate the tracking object according the scale and orientation . This algorithm can tracking the object adaptively the change of direction and scale . Compressive random projection is a powerful appearance model to derive effective Haar-like features from non-rotated 4D rectangles , which can support fast and reliable object tracking . In this paper , we show that such successful compressive tracking scheme can be further significantly improved by structural regularization . Specifically , we propose two effective structural regularizations . Overall , the result of sensitivity highest came from Kirsch Templates ( 96.928 ) , while specificity from Fuzzy C means ( 77.573 ) . However , in term of accuracy average , the Line Tracking method is more successful compared to the other methods . An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles . The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions . Experimental results show that it achieves robust pattern extraction , and the equal error rate was 0.145 % in personal identification . But another aspect that is at least equally important has been rather neglected : Internet privacy . On-line tracking can be used for legitimate purposes ( e.g. preventing illegitimate login attempts ) , but it also invades the privacy of the users . This paper focuses on browser fingerprinting , which is the least known threat to privacy among Internet users , presenting it in detail , from definition to the possible countermeasures . From the viewpoint of an assist system for human motion , a unified control of posture and trajectory brings a sophisticated function to a bicycle , and a high-performance bicycle is expected to be a convenient vehicle , similar to a small car . This paper proposes two strategies to stabilize bicycle posture and trajectory control that realizes a straight-line tracking : one is a lateral velocity controller , and the other is a steering function controller . The validity of the proposed approaches is evaluated by simulations and experiments . In this paper we propose a method able to identify the presence of objects remaining motionless in the scene for a long time by analyzing the videos acquired by surveillance cameras . Our approach combines a background subtraction strategy with an enhanced tracking algorithm . The main contributions of this paper are the following : first , spatio-temporal information is implicitly encoded into a heat map ; furthermore , differently from state of the art methodologies , the background is not updated by only evaluating the instantaneous movement of the objects , but instead by taking into account their whole history encoded in the heat map . In contrast with the traditional method where only the laser geometry is independently calibrated , the proposed method simultaneously optimizes all the laser parameters and directly associates the parameters with a motion sensing model . By jointly considering the consistency among laser Doppler velocimetry , the laser geometry and a visual marker tracking system , the proposed calibration method further boosts the accuracy of MLDSS . We analyzed the factors influencing the precision , and quantitatively evaluated the efficiency of the proposed method on several data sets . This advanced mobile C-arm system acquires registered X-ray and optical images by construction , which facilitates the generation of panoramic X-ray images based on the motion estimation of the X-ray source . Visual marker tracking is employed to estimate the camera motion and this estimated motion is also applied to the X-ray source . Our proposed method is suitable and practical for intra-operative usage generating panoramic X-ray images without the requirement of a fronto-parallel setup and overlapping X-ray images . By implementing a tracking and verification system , clinical facilities can effectively monitor workflow and heighten information security in today 's growing demand towards digital imaging informatics . This paper presents the technical design and implementation experiences encountered during the development of a Location Tracking and Verification System ( LTVS ) for a clinical environment . In this paper a novel bottom-up and top-down integrated approach is proposed to solve the problem of dynamic context driven visual processing in meeting scenarios . A set of visual detection , tracking and verification modules are effectively organized to extract rough-level visual information , based on which a bottom-up context analysis is performed through Bayesian Network . In reverse , results of scene analysis are applied as top-down guidance to control refined level visual processing . Function keys allow the consultant to modify the entry , export it to a file , or to send E-Mail to the individuals involved with the problem entry . Once the client and transaction tracking parts of the system are implemented , clicking the “ Clients ” button will provide information on clients who asked the particular question , and choosing “ Transactions ” will list the client-consultant transaction where the question was asked . What kinds of data are stored ? The variables of interest are superficial velocities of the fluids , liquid slug and gas bubble lengths , and the bubble translational velocity and their standard deviations . The knowledge and understanding of those parameters will improve the characterization of the intermittent slug flows and will also provide information on the development of physical models that describe this phenomenon , such as the unit cell models , the drift flux model and the slug tracking model . In general , the estimation models based on ANNs showed good results compared with reference values obtained experimentally . Smart factory has the function of integrated management of production process management , logistics management as a intelligent factory , it is also emerging as the core of new industry which converges ICT and manufacturing business . We suggested Smart factory logistics management system which embedded position tracking technology and the system converges ICT and IoT. This suggested system can manage all the processes from production to release by tracking route and position based on signal strength of bluetooth 4.0 beacon tag . For the more , we will expect to apply to the various type of factory environments like detachable installation , optimized management using sensor . We present a design of a tracking controller for an unstable , nonminimum phase nonlinear plant using trajectory linearization method . The controller consists of two parts : a dynamic inverse and a tracking error stabilizing control law . The nominal control computed by nonlinear pseudo-inverse using nonlinear coordinate transformation and backstepping stabilization , and trajectory stabilization is achieved using linear time-varying ( LTV ) PD-eigenstructure assignment . This is followed by a manifesto that articulates design goals for producing devices linking these trends , creating a space for interaction design research . We end with a discussion of Ovum , an example of a careful fertility tracking device . Besides , multicamera applications often require a trade-off between accuracy and communication load within a camera network . In this article , we present a real-time distributed multicamera tracking system for the analysis of people in a meeting room . One contribution of the article is that we provide a scalable solution using smart cameras . We are developing an automated vessel-tracking method based on the double-square-box region-of-search technique , for efficient tracking of the connected vascular tree in a digital subtraction angiography ( DSA ) image . Tracking points and branch vessels are located by searching of the perimeter of boxes , which are centered on previously determined tracking points .
In this paper , we present lesion detection schemes in dermoscopy images on mobile platforms . The systems are based on density based clustering ( DBSCAN ) and fuzzy c-means ( FCM ) clustering and developed for Windows Phone and Android environments . We tested the systems on dermoscopy images and ROIs are successfully extracted .
The problem occurs due to dispatching taxis is not in line with the taxi demand in different functional regions during different time periods . The method proposed in this paper clustered taxi Origin-Destination data points of the functional region with the improved DB-SCAN algorithm and yielded the areas of interest with the statistical method named Chi-square distribution . The accuracy and credibility of the method was evaluated through two indicators called coverage and hit ratio and a comparison analysis on situations of different regions .
Noninvasive BCI techniques play a major role for this purpose . In this paper , we have proposed three algorithms of cursor movement using three well-known clustering methods ( Minimum distance , DB-Scan and Gaussian Mixture Model ) . These proposed techniques are generally tested on motor imagery EEG data .
The second makes use of transfer functions as a mechanism to generate binary versions . The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function ( TF ) and random operators .
The Alisan Creek watershed in central Taiwan was one of serious hazard areas . This study proposed ( 1 ) two-layer SOM-based classifier coupled with pre- and post-typhoon SPOT satellite images to extract accurate landslide , ( 2 ) the concept of landslide slope unit ( LSU ) derived from automated watershed modeling delineation to calculate spatial characteristics of the landslide and ( 3 ) DBSCAN-based model to analyze the spatial correlations at landslides to assess the priority of landslide treatment sites . The evaluated result shows that the landslide area soon after the typhoon is 619.91 ha ( Kappa coefficient 0.9766 ) .
The aim of this paper is to propose a method to cluster GPS data corresponding to driving destinations . A new DBSCAN-based algorithm is proposed to group stationary GPS traces , collected prior to end of trips , into destination clusters . While the original DBSCAN clustering algorithm uses a global threshold as a closeness measure in data space , we develop a method to set local thresholds values for data points ; this is important because the GPS data proximity strongly depends on the density of the street grid around each point .
During the mapping procedure , the partitioned segments are mapped into the corresponding cells , and the mapping relationship between the segments and the cells are stored . In the clustering phase , adopting the DBSCAN-based method , the segments in the cells are clustered on the basis of the calibrated values of parameters from the mapping procedure . The extensive experiments indicate that although the results of the adaptive parameter calibration are not optimal , in most cases , the difference between the adaptive calibration and the optimal is less than 5 % , while the run time of clustering can reduce about 95 % , compared with the TRACLUS algorithm .
This paper proposes DBSCAN-SI that is an algorithm for clustering with influences of spatial objects . DBSCAN-SI that is extended from existing DBSCAN and DBSCAN-W converts from non-spatial properties to the influences of spatial objects during the spatial clustering . It increases probability of inclusion to the cluster according to the higher the influences that is affected by the properties used in clustering and executes the clustering not only respect the spatial distances , but also volume of influences .
Abstract Based on the DBSCAN analysis , kernel density analysis and word cloud analysis , this paper analyzed the composition types and spatio-temporal evolution characteristics of catering points and catering clusters form macro , meso and micro dimensions within 5th ring road in Beijing with the caterings POI data in 2012 , 2014 and 2016 . The conclusions are as follows : first : ( 1 ) The number of total catering POI increased by about 6 times , and the center of the whole data shows a tendency to move from west to southeast .
Average silhouette width score is used here to compare the quality of clusters formed by both algorithms . By testing 2DBSCAN-LOD on different artificial datasets , it is found that the average silhouette width score of clusters formed by DBSCAN-LOD is higher than that of the clusters formed by DBSCAN .
Density-based spatial clustering of applications with noise ( DBSCAN ) is the most commonly used density-based clustering algorithm , where it can discover multiple clusters with arbitrary shapes . DBSCAN works properly when the input data type is homogeneous , but the DBSCAN 's approach may not be sufficient when the input dataset has textual heterogeneity ( e.g. , when we intend to find clusters from geo-tagged posts on social media relevant to a certain point-of-interest ( POI ) ) , thus leading to poor performance . In this paper , we present DBSTexC , a new density-based clustering algorithm using spatio -- textual information on Twitter .
This may result in the system being abandoned , or worse , waste the defender ’s time investigating false alarms . While our attacks generalize to all DBSCANbased tools , we focus our evaluation on AnDarwin , a tool designed to detect plagiarized Android apps . We show that an adversary can merge arbitrary clusters by connecting them with “ bridges ” , that even a small number of merges can greatly degrade clustering performance , and that the defender has limited recourse when relying solely on DBSCAN .
It is divided into the following steps : 1 ) partitioning the raw data based on a random sample , 2 ) computing local DBSCAN algorithms in parallel , 3 ) merging the data partitions based on the centroid . Compared with the traditional DBSCAN algorithm , the experimental result shows the proposed S_DBSCAN algorithm provides better operating efficiency and scalability .
Radio Frequency Identification RFID system is a low-cost contactless automatic identification technology ; and barcode as a traditional technology is now broadly replaced by RFID systems to make objects more manageable in supply chains and other enterprises . This technology is operational in open wireless communication spaces whereby its transmission signals can be easily accessed resulting in security problems .
As the radio frequency identification ( RFID ) technology continues to evolve and mature , RFID tags can be implemented in a wide range of applications . Due to the shared wireless medium between the RFID reader and the RFID tag , adversaries can launch various attacks on the RFID system .
In this paper , we propose a novel radio-frequency identification (RFID)-based methodology that can estimate the position and orientation of an object . Unlike other RFID-based localization systems , the proposed method employs a single nonsteered stationary antenna and three passive tags mounted on each object .
Indoor/outdoor localization has gained importance as it has the potential to improve various processes related to the resource management of construction projects and to deliver personalized and location-based services ( LBS ) . Radio Frequency Identification ( RFID ) based systems , have been widely used in different applications in construction and maintenance . This paper investigates the usage of active RFID technology for the localization of movable objects ( e.g. material , equipment , tools , and assets ) equipped with RFID tags using handheld readers .
The radio frequency identification system ( RFID ) is becoming one of the most popular system in wireless technologies . The UHF RFID tag emulator is a part of RFID testing tools .
Abstract The present article is devoted to the issue of improvement of utilization efficiency of modern information technologies in order to enhance the quality and safety of healthcare delivery . The introduction of Real-time Locating Systems ( RTLS ) , in particular Radio Frequency Identification System ( RFID ) , applied in conjunction with intelligent data processing system can reduce the severity of the consequences of event risks implementation , as well as avoidance of a number of such consequences . The authors of the present article suggest using mathematical tools of fuzzy logic theory for event analysis in online mode and recommendation formation on their basis for timely management decision-taking .
A human object tracking algorithm fused with depth information , color information and forecast information is presented in this paper , which enables follow-robot to track motion human object robustly . Firstly , Camshift algorithm is expended into three-dimensional space by color information and depth information . Secondly , the interference from complex background is eliminated from the projection drawing by depth image of motion human object in three-dimensional space .
This paper presents the results of a factorial experiment performed to determine the effect of illumination compensation methods with histogram back projection to be used for object tracking algorithm continuous adaptive mean-shift ( Camshift ) . Since Camshift tracking can be used for distance approximation of an object , a precise tracker algorithm is required .
In order to achieve intelligent feedback tracking of moving target , an embedded system for recognition and tracking of moving target based on the DSP chip and Camshift algorithm was designed . The system uses frame difference method to recognize the moving target and Camshift algorithm to track the moving target .
Condensation algorithm is used for estimating the states of the targets and CAMShift is used for data association . We have used ConDensation algorithm for multiple target tracking in single camera FoV , where the data association is done using the search window approximated by the CAMshift filter for each target . It takes care of errors in tracking which may arise due to the splitting of targets , a common phenomena in detection of articulated objects .
There are several non-invasive and computationally inexpensive techniques for face tracking that draw upon algorithms from computer vision . Of them , Bradski 's CAMSHIFT algorithm is appealing because it requires minimal training . These techniques are particularly attractive in light of the growing installed base of fast desktop computers and cheap , low-end desktop digital video cameras .
This paper propose a real-time combined method of Camshift [ 1 ] algorithm and Haar-like feature detection [ 2 ] for tracking and recognizing hand gesture in images acquired by a possibly moving camera . A Haar-like classifier is used during the initializing of the system to acquire the user 's hand color .
Finally , it makes comparative experiments between the improved algorithms and general Camshift . The experimental results show that , combined with the background difference method and the continuous Camshift algorithm for tracking of moving objects , to some extent it can meet the real-time and stability requirements .
In this work , we explore video processing for driving assistance system . An object ’s detection and tracking system based on the Faster R-CNN and Camshift algorithm is proposed , and Kalman filtering algorithm is used to predict the position of objects . Our system differs with other object-tracking algorithms based on deep learning , which directly detects each frame and has no tracking part , such as YOLO and SSD .
Multi-cue-based Camshift algorithm is presented for object tracking in complex scenes . Because color-based Camshift algorithm suffers from objects with color clutter in backgrounds , this paper integrates motion cue with color cue to extend its application areas .
To realize the accurate recognition of manual alphabets in the sign language video , this paper presentes an improved algorithm based on DI _ CamShift ( depth image CamShift ) and SLVW ( sign language visual word ) multi-features combine . First , the video and depth image information of sign language gestures are obtained by usesing Kinect .
Camshift could accurately follow the moving target under the simple background , but at a complicated one it was very difficult for Camshift to accurately track a target . Predicted Camshift was used to track the real-time moving target in this paper . It takes the centroid of the Camshift as the observation signal , and adopts the Kalman filter to predict and correct the targetpsilas centroid .
In this paper we describe a Camshift implementation on mobile robotic system for tracking and pursuing a moving person with a monocular camera . Camshift algorithm uses color distribution information to track moving object .
This paper presents an efficient combination of two well-known tracking algorithms , Tracking-Learning-Detection ( TLD ) and Compressive Tracking ( CT ) to devise an algorithm which takes advantages of both and outperforms them on their short-ends by virtue of other . TLD fails in cases including full out-of-plane rotation , fast motion and articulated object tracking .
To solve the problems of tracking errors such as target missing that emerged in compressive tracking ( CT ) algorithm due to factors such as pose variation , illumination change , and occlusion , a novel tracking algorithm combined angular point matching with compressive tracking ( APMCCT ) was proposed . A sparse measurement matrix was adopted to extract the Haar-like features .
Building on our previous video-to-text ( V2 T ) Fusion2014 paper , we extend the method for evaluation of eight tracking methods compared for extraction of semantic information including target number , category , attribute , and direction . Using the CMUSphinx speech-to-text system for semantic parsing of user call-outs , preliminary results show the integration of video tracking and text analysis is better with the compressive tracker ( CT ) and the Tracking-Learning-Detection ( TLD ) method . The feature analysis of the CT and TLD demonstrate the ability to associate user call-out text-based semantic descriptors with video exploitation .
In order to solve the compressive tracking algorithm can not solve the change of scale and rotation in object tracking , an adaptive compressive tracking algorithm based on SIFT features is proposed . Firstly , we using SIFT feature extracted the scale-space extremes points of the object model and the tracking frame , Then the scale and rotation angle of the object being tracked were calculated by using the scale and angle information which was provided by the feature points correctly matched between model and the frame , we calculate the main-orientation information of the object and the scale of object according those refined scale-space extremes point , then using compressive sense tracking algorithm to locate the tracking object according the scale and orientation . This algorithm can tracking the object adaptively the change of direction and scale .
Compressive random projection is a powerful appearance model to derive effective Haar-like features from non-rotated 4D rectangles , which can support fast and reliable object tracking . In this paper , we show that such successful compressive tracking scheme can be further significantly improved by structural regularization . Specifically , we propose two effective structural regularizations .
Overall , the result of sensitivity highest came from Kirsch Templates ( 96.928 ) , while specificity from Fuzzy C means ( 77.573 ) . However , in term of accuracy average , the Line Tracking method is more successful compared to the other methods .
An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles . The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions . Experimental results show that it achieves robust pattern extraction , and the equal error rate was 0.145 % in personal identification .
But another aspect that is at least equally important has been rather neglected : Internet privacy . On-line tracking can be used for legitimate purposes ( e.g. preventing illegitimate login attempts ) , but it also invades the privacy of the users . This paper focuses on browser fingerprinting , which is the least known threat to privacy among Internet users , presenting it in detail , from definition to the possible countermeasures .
From the viewpoint of an assist system for human motion , a unified control of posture and trajectory brings a sophisticated function to a bicycle , and a high-performance bicycle is expected to be a convenient vehicle , similar to a small car . This paper proposes two strategies to stabilize bicycle posture and trajectory control that realizes a straight-line tracking : one is a lateral velocity controller , and the other is a steering function controller . The validity of the proposed approaches is evaluated by simulations and experiments .
In this paper we propose a method able to identify the presence of objects remaining motionless in the scene for a long time by analyzing the videos acquired by surveillance cameras . Our approach combines a background subtraction strategy with an enhanced tracking algorithm . The main contributions of this paper are the following : first , spatio-temporal information is implicitly encoded into a heat map ; furthermore , differently from state of the art methodologies , the background is not updated by only evaluating the instantaneous movement of the objects , but instead by taking into account their whole history encoded in the heat map .
In contrast with the traditional method where only the laser geometry is independently calibrated , the proposed method simultaneously optimizes all the laser parameters and directly associates the parameters with a motion sensing model . By jointly considering the consistency among laser Doppler velocimetry , the laser geometry and a visual marker tracking system , the proposed calibration method further boosts the accuracy of MLDSS . We analyzed the factors influencing the precision , and quantitatively evaluated the efficiency of the proposed method on several data sets .
This advanced mobile C-arm system acquires registered X-ray and optical images by construction , which facilitates the generation of panoramic X-ray images based on the motion estimation of the X-ray source . Visual marker tracking is employed to estimate the camera motion and this estimated motion is also applied to the X-ray source . Our proposed method is suitable and practical for intra-operative usage generating panoramic X-ray images without the requirement of a fronto-parallel setup and overlapping X-ray images .
By implementing a tracking and verification system , clinical facilities can effectively monitor workflow and heighten information security in today 's growing demand towards digital imaging informatics . This paper presents the technical design and implementation experiences encountered during the development of a Location Tracking and Verification System ( LTVS ) for a clinical environment .
In this paper a novel bottom-up and top-down integrated approach is proposed to solve the problem of dynamic context driven visual processing in meeting scenarios . A set of visual detection , tracking and verification modules are effectively organized to extract rough-level visual information , based on which a bottom-up context analysis is performed through Bayesian Network . In reverse , results of scene analysis are applied as top-down guidance to control refined level visual processing .
Function keys allow the consultant to modify the entry , export it to a file , or to send E-Mail to the individuals involved with the problem entry . Once the client and transaction tracking parts of the system are implemented , clicking the “ Clients ” button will provide information on clients who asked the particular question , and choosing “ Transactions ” will list the client-consultant transaction where the question was asked . What kinds of data are stored ?
The variables of interest are superficial velocities of the fluids , liquid slug and gas bubble lengths , and the bubble translational velocity and their standard deviations . The knowledge and understanding of those parameters will improve the characterization of the intermittent slug flows and will also provide information on the development of physical models that describe this phenomenon , such as the unit cell models , the drift flux model and the slug tracking model . In general , the estimation models based on ANNs showed good results compared with reference values obtained experimentally .
Smart factory has the function of integrated management of production process management , logistics management as a intelligent factory , it is also emerging as the core of new industry which converges ICT and manufacturing business . We suggested Smart factory logistics management system which embedded position tracking technology and the system converges ICT and IoT. This suggested system can manage all the processes from production to release by tracking route and position based on signal strength of bluetooth 4.0 beacon tag . For the more , we will expect to apply to the various type of factory environments like detachable installation , optimized management using sensor .
We present a design of a tracking controller for an unstable , nonminimum phase nonlinear plant using trajectory linearization method . The controller consists of two parts : a dynamic inverse and a tracking error stabilizing control law . The nominal control computed by nonlinear pseudo-inverse using nonlinear coordinate transformation and backstepping stabilization , and trajectory stabilization is achieved using linear time-varying ( LTV ) PD-eigenstructure assignment .
This is followed by a manifesto that articulates design goals for producing devices linking these trends , creating a space for interaction design research . We end with a discussion of Ovum , an example of a careful fertility tracking device .
Besides , multicamera applications often require a trade-off between accuracy and communication load within a camera network . In this article , we present a real-time distributed multicamera tracking system for the analysis of people in a meeting room . One contribution of the article is that we provide a scalable solution using smart cameras .
We are developing an automated vessel-tracking method based on the double-square-box region-of-search technique , for efficient tracking of the connected vascular tree in a digital subtraction angiography ( DSA ) image . Tracking points and branch vessels are located by searching of the perimeter of boxes , which are centered on previously determined tracking points .
Automatic mobile segmentation of dermoscopy images using density based and fuzzy c-means clustering Taxi origin-destination areas of interest discovering based on functional region division Multi-target way of cursor movement in brain computer interface using unsupervised learning A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem Using SOM and DBSCAN-based models for landslide hazard and spatial correlations analysis: A case study in central Taiwan Clustering Driving Destinations Using a Modified DBSCAN Algorithm with Locally-Defined Map-Based Thresholds An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis Design and Development of Clustering Algorithm Considering Influences of Spatial Objects Temporal and Spatial Variation Characteristics of Catering Facilities Based on POI Data: A Case Study within 5th Ring Road in Beijing 2DBSCAN with Local Outlier Detection DBSTexC: Density-Based Spatio-Textual Clustering on Twitter Attacking DBSCAN for Fun and Profit A Parallel DBSCAN Algorithm Based on Spark Critical Evaluation of RFID Security Protocols An Efficient RFID Authentication Protocol Providing Strong Privacy and Security Novel RFID-Based Pose Estimation Using Single Stationary Antenna Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags FSM based Manchester encoder for UHF RFID tag emulator Intelligent data processing received from radio frequency identification system Multi-information Fusion Algorithm for Human Target Tracking THE EFFECT OF ILLUMINATION COMPENSATION METHODS WITH HISTOGRAM BACK PROJECTION FOR CAMSHIFT APPLICATION Camshift algorithm -based moving target recognition and tracking system Object Matching Across Multiple Non-overlapping Fields of View Using Fuzzy Logic The Effects of Capture Conditions on the CAMSHIFT Face Real-time gesture recognition system based on Camshift algorithm and Haar-like feature Moving object tracking based on improved Camshift algorithm Tracking System for Driving Assistance with the Faster R-CNN Robust object tracking using camshift with multicue integration Improved Manual Alphabet Recognition Method Based on Multi-features Fusion Target tracking using predicted Camshift Implementing Camshift on a Mobile Robot for Person Tracking and Pursuit Integrated algorithm for different tracking challenges Target Tracking Algorithm Using Angular Point Matching Combined with Compressive Tracking Video-to-text information fusion evaluation for level 5 user refinement Adaptive Compressive Tracking Algorithm Based on SIFT Features Structure-regularized compressive tracking A Comparative Study of Different Blood Vessel Detection on Retinal Images Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification Assuring Privacy in Surfing the Internet A Study on Straight-Line Tracking and Posture Control in Electric Bicycle A Method for Detecting Long Term Left Baggage based on Heat Map Visual Calibration for Multiview Laser Doppler Speed Sensing Long Bone X-ray Image Stitching Using C-arm Motion Estimation Technical experiences of implementing a wireless tracking and facial biometric verification system for a clinical environment Dynamic context driven human detection and tracking in meeting scenarios HDD: a helpdesk database Two-Phase Slug Flow Characterization Using Artificial Neural Networks 블루투스 비콘 기반 실내위치추적기술을 활용한 스마트 팩토리 물류관리시스템 Unstable, nonminimum phase, nonlinear tracking by trajectory linearization control Careful Devices Low-complexity scalable distributed multicamera tracking of humans Automated tracking and computer reproduction of vessels in DSA images.
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