Rather than using heavily parameterized models for the tracking of foreground regions , we suggest the modeling of objects based on the bounding boxes that contain them . The algorithm describes a novel integration of dynamic reference frame differencing and coarse motion estimation to overcome the various occlusion problems encountered in multiple object tracking . Change detection is performed by taking the difference between the current frame and a dynamic reference frame , where the reference frame is adaptively updated over time to account for changes in the background , illumination variations , and the like . Tracking by detection is a common approach to solving the Multiple Object Tracking problem . In this paper we show how deep metric learning can be used to improve three aspects of tracking by detection . Such direction-only sensors can be used in applications with the aim of tracking multiple moving objects . One of the reasons why multiple moving object tracking can be challenging is because of the need to deal with the problem of pairing sensors measurements with tracked objects in the presence of clutter ( the data association problem ) . In this paper we propose to approach the problem of multiple object tracking in clutter with direction-only data by setting it on the unit sphere , thus tracking the objects with a Bayesian estimator based on the von Mises-Fisher distribution and probabilistic data association . Vehicles are detected as moving objects using foreground and background image segmentation . Adjacent pixels in the moving objects image are grouped together and a weight factor based on cluster area , cluster overlapping area and distance between multiple clusters is computed to enable multiple moving object tracking . To ensure real time capabilities , image processing algorithm computation distribution between CPU and GPU is applied . We make use of a database that involves all previously detected descriptors , forming therefore a discrete search space where the sought solution is a descriptor and its quality is represented by its similarity to the query profile . The approach is evaluated within a multiple object tracking scenario , and a validation process using the normalized cross correlation method to accept or reject the obtained reidentification results is included . The experimental results show promising performances in terms of computation cost as well as reidentification rate . In this respect , our approach provides a way to obtain a new particle approximation , which is better in a precise information-theoretic sense than that of any of the particle filters alone . The viability of the proposed approach is demonstrated in a multiple object tracking scenario . Abstract This paper addresses the problem of tracking space-borne objects of interest ( OoIs ) with a satellite-assisted collaborative space surveillance network ( SSN ) . It is formulated as a multiple moving-object tracking problem in a hybrid distributed wireless sensor network ( WSN ) made of both mobile and static nodes . The static nodes may be considered as ground-based sensors with fixed locations , while mobile sensor nodes can be satellites in space . In our previous work , we proposed an approach to particle filtering which simultaneously adjusts the proposal variance and number of particles for each frame , in order to minimize the tracking distortion for single object tracking . In this paper , we extend our previous work to multiple object video tracking . Under the framework of distributed multiple object tracking , we propose the tracking distortion and use rate distortion theory to derive the optimal particle allocation among multiple targets as well as multiple frames . In the paper , we achieved the goal in two respects . Regarding the data processing , we studied how to efficiently process tracking data by utilizing the parallel characteristics of iDMA ( integrated direct memory access ) and a DSP core ; and regarding the data storage , we proposed a time-sharing strategy to solve the DSP local memory ( data RAM ) usage issue for multiple tracking objects . In addition , regarding the software design , we propose a new strategy , which includes two levels of parallel computations : the frame-level parallel computations and the tracking object-level parallel computations . We use the single shot setting . DukeMTMC - reID is a subset of the newly - released multi - target , multi - camera pedestrian tracking dataset . The original dataset contains eight 85 - minute high - resolution videos from eight different cameras . In recent years , Visual Question Answering ( VisualQA ) has gradually become one of the research hotspots of video understanding , but most of the researches are mainly focused on Image Question Answering ( ImageQA ) , while fewer researches pay attention to Video Question Answering ( VideoQA ) . Inspired by the ImageQA model , we propose a model , which utilizes videos and questions to generate answers . Region of interest extraction is very important in video analysis and understanding . A simple and fast region of interest extraction method for sport scene images is proposed in this paper . Functional object recognition in video is an emerging problem for visual surveillance and video understanding problem . By functional objects , we mean objects with specific purpose such as postman and delivery truck , which are defined more by their actions and behaviors than by appearance . The model is expected to bring the following advantages \2011\202 adequate mixtures of stochastic dynamics and deterministic one can gain representation power of the model , \2012\202 no needs for arbitrary manipulation of data as well as interpretation of them by human , \2013\202 possibility for scaling of the model by incorporating with the scheme of multiple timescales dynamics for extracting temporal hierarchy from the data . The potential impacts by applying the model to sensory-motor sequence learning by robots as well as video image understanding by accumulated learning of the exemplars are discussed . Human action recognition ( HAR ) has emerged as a core research domain for video understanding and analysis , thus attracting many researchers . Although significant results have been achieved in simple scenarios , HAR is still a challenging task due to issues associated with view independence , occlusion and inter-class variation observed in realistic scenarios . Multiple object tracking ( MOT ) is an important yet challenging task in video understanding and analysis . Basically , MOT aims to associate detected objects into trajectories based on their temporal relationships . The approach is able to give good estimates of object dimensions and proposes visual reliability measures for the object dimensions . These measures give a representation of the visibility of the estimated dimension and are principally proposed to aid posterior phases of the video understanding process , as object tracking and event detection . The method obtains the 3D parallelepiped model estimation using a set of 2D moving regions ( obtained in a segmentation phase ) , the perspective matrix transform ( obtained from camera calibration using the pin-hole camera model ) and predefined 3D models of expected objects in the scene . This approach comprises motion segmentation , object classification , tracking and event learning phases . This work is focused on building the first fundamental blocks allowing a proper management of uncertainty of data in every phase of the video understanding process . The main contributions of the proposed approach are : ( i ) a new algorithm for tracking multiple objects in noisy environments , ( ii ) the utilisation of reliability measures for modelling uncertainty in data and for proper selection of valuable information extracted from noisy data , ( iii ) the improved capability of tracking to manage multiple visual evidence-target associations , ( iv ) the combination of 2D image data with 3D information in a dynamics model governed by reliability measures for proper control of uncertainty in data , and ( v ) a new approach for event recognition through incremental event learning , driven by reliability measures for selecting the most stable and relevant data . A key task scheduler is designed for the parallelization of computation tasks . The SVM method is used to train models for video understanding purposes . To effectively shorten the training and processing time , the hybrid computing framework is used to train large scale SVM models . Video interpretation and understanding is one of the long-term research goals in computer vision . Realistic videos such as movies present a variety of challenging machine learning problems , such as action classification/action retrieval , human tracking , human/object interaction classification , etc . Furthermore , we also present two semantic object segmentation methods , Boundary Neural Fields ( BNFs ) , and Convolutional Random Walk Networks ( RWNs ) , which integrate low-level affinity cues into an object segmentation process . We then shift our focus to video-level understanding , and present a Spatiotemporal Sampling Network ( STSN ) , which can be used for video object detection , and discriminative motion feature learning . Afterwards , we transition into the second subproblem of ` ` seeing to learn ' ' , for which we leverage first-person GoPro cameras that record what people see during a particular activity . Object detection is an important yet challenging task in video understanding & analysis , where one major challenge lies in the proper balance between two contradictive factors : detection accuracy and detection speed . In this paper , we propose a new adaptive patch-of-interest composition approach for boosting both the accuracy and speed for object detection . On the other hand , a large number of tools have been proposed in the literature that handle specific segmentation tasks very well . Dynamic segmentation is a possible solution , where the image is split recursively , in a hierarchical fashion , and different tools are used at each step to address specific segmentation tasks . In this work , the segmentation of a high-resolution test image is used as a running example and as a proof of concept of the potential of this approach . Dynamic segmentation has been neither well specified nor systematically treated in terms of algorithmic development . Vendors have responded to the demand for dynamic segmentation in different ways . Although each vendor has interpreted the problem differently and has invented slightly different solutions , the differences are subtle and difficult to assess without head-to-head comparisons . An image sensor having a resolution of 1600 by 1200 pixels is used to capture images of symbols in complex backgrounds . Dynamic image segmentation , component labeling and feature extraction was performed on the FPGA . The region of interest was further computed from the extracted features . The dynamic object segmentation in videos taken from a static camera is a basic technique in many vision surveillance applications . In order to suppress fake objects caused by dynamic cast shadows and reflection images , this paper presents a novel segmentation model with the function of cast shadow and reflection image suppression . A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues . Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation . key words : video object segmentation , salient object-like proposal , nonlinear regressor , dynamic scene , random forest Introduction Imagine a scenario where an autonomous robot is wondering in an unknown environment , the location information of foreground targets which can be inferred with an unsupervised method will certainly facilitate , to a large extent , subsequent recognition , tracking as well as visual servo manipulation of the targets . Despite the research efforts in dynamic scene segmentation , designing an algorithm that is robust for a wide variety of dynamic scenes that involve complex natural environments is still an open problem . The bottle neck in dynamic scene segmentation is the foreground object hypothesis stage . As stated in several works , static time-window length has several drawbacks while adjusting dynamically the window length is more appropriate . However , most of the previous methods for dynamic data segmentation are based on two strong assumptions : the user 's routine does not change and a pre-segmented data set can be provided for learning the time-window size . Yet , these constraints are not always verified . As stated in several works , static time-window length has several drawbacks while adjusting dynamically the window length is more appropriate . However , each of previous methods for dynamic data segmentation targets only a particular type of application . Hence , there is a need for a general method independent of applications providing high degree of usability . Each cluster contributes a node along the centerline , which is created by connecting successive nodes until all object voxels are exhausted . While data segmentation is independent of this algorithm , we illustrate the use of the ISODATA method to achieve dynamic ( local ) segmentation . Diameter estimation at each node is calculated using the Rayburst Sampling algorithm , and spurious end nodes caused by surface irregularities are then removed . In this article , a novel strategy is designed to handle this problem . We leverage both the object detection result and observation information in a Bayesian framework for dynamic region detection . Once the dynamic regions are determined , we discard them and feed the other regions in a state-of-the-art visual simultaneous localization and mapping system for further visual localization . Now this technique has been used in some GIS software represented by Arc/Info which is based on the " Arc-Node " data model , but the research on using dynamic segmentation in some GIS software with the " entity oriented " data model is seldom . This paper analyses dynamic segmentation data structure in Arc/Info and discusses how to implement the dynamic segmentation in MapInfo . Dynamic segmentation is an important technique in dynamic analysis , displaying linear features in Geographic Information Systems for transportation ( GIS-T ) and Intelligent Transport Systems ( ITS ) . In this paper , the authors present brief surveys of work in dynamic segmentation development , then present a spatial-temporal dynamic segmentation model . In the conceptual model , components of a dynamic segmentation system merge into a temporal factor . Retrieving videos by key words requires semantic knowledge of the videos . However , manual video annotation is very costly and time consuming . Most works reported in literatures focus on annotating a video shot with either only one semantic concept or a fixed number of words . TBased on the user needs and intelligent terminal , trends in the new media industry are analyzed in this paper . Then , the demand of the video industry chain is discussed , and the advantages of telecom operators in the video industry chain is summarized mainly in sales and content distribution channels . The practice of exploring China Mobile , China Unicom , China Telecom is also introduced . Experimental results show that combining multiple modes of information is more effective in detecting cuts and dissolves . The proposed technique is capable of processing video frames in real time and could be used for the rapid generation of key frames for real-time browsing of video streams and for indexing to support content-based access to video libraries . Experimental results show that the algorithm is effective in detecting abrupt scene changes ( cuts ) as well as gradual scene changes ( dissolves ) in an MPEG1-coded video stream . The algorithm is capable of processing video frames in real time and could be used for the rapid generation of key frames for real-time browsing of video streams and for indexing to support content-based access to on-line video libraries . As an example , an error resilient frame-level bit allocation solution is proposed based on the model . Simulation results demonstrate that for lossy video transmission , the proposed method can achieve more consistent and higher overall visual qualities than that of the other nonerror-resilient schemes , which proves the remarkable potential of the new model . The apparatus is a video playback server which classifies and stores multiple videos by category and plays back a corresponding video in response to a playback request by a smartphone ; the smartphone which connects to the video playback server and requests playback of a video that a user selected ; an uploading terminal which uploads a video to fit in a selected category to the video playback server ; a ranking server which counts the number of playbacks of videos played in the video playback server and determines the rankings of videos in each category ; and a combining server which combines videos corresponding to top rankings by referring to the rankings in each category , and stores the combined video in the video playback server . The combining server combines the videos in order of the rankings , and stores the names of the videos used for video combination and the playback time of each of the videos in a header . The video playback server provides a skip button when the combined video is played back and changes a playback position to a start position of each of the videos when the skip button is selected . Abnormal event detection from video surveillance is a key issue for social security . At present , the challenge lies in the effective feature extraction of video data . In order to solve the problem , a deep learning method based on convolutional autoencoder was proposed in this paper . By the methods of theoretic analysis , video watch , mechanics analysis etc , this essay analyzes single hand up shoulder method of shooting , puts great emphasis to analyze the influenced to the clout the holding of rate the ball the method , raises the elbow to stretch the arm , bends the wrist to stir to finger , which propose and pays attention to method of catching , increases the hand the high degree , the bend wrist stir to point and take aim at the integral to increase to shooting percentage and the anti interference of shooting . This paper describes a 10 bit 30 Msample/s ( MSPS ) CMOS analog-to-digital converter ( ADC ) for high-speed signal processing , especially for subsampling applications , for example digital video broadca ... The introduction of the sequential and concurrent flow of resources in human cognition and behavior can significantly improve the accuracy and preparation time for intention prediction . This paper introduces a threaded interaction model and applies it to user intention prediction for initial delay reduction in web video access . The proposed technique consists of a candidate selection module , a decision module and a preparation module that prefetches and preloads the web video data before a user 's click action . The coordination algorithm relies on simple compiler transformations to track the progress of the processes , and performs the adaptive changes locally and asynchronously at each process . Measurements of the runtime overhead of the automatic coordination algorithm for two adaptive applications ( a parallel PDE solver and a distributed video tracking code ) show that the overhead is less than 1 % of execution time for both these codes , even with relatively frequent adaptations , and does not grow significantly with the number of coordinating processes . As shown in Figure [ reference ] , our method can reconstruct sharper and more accurate images than the state - of - the - art approaches . subsection : Super - resolving video sequences We conduct frame - based SR experiments on two video sequences from with a spatial resolution of pixels . He received a PhD degree from the University of Cambridge in 2002 , where he worked on 3D reconstruction of architecture from images . His research interests include image - based modeling , automated video surveillance , and image search . Anton van den Hengel is a Professor at the University of Adelaide and the founding Director of The Australian Centre for Visual Technologies ( ACVT ) .
Rather than using heavily parameterized models for the tracking of foreground regions , we suggest the modeling of objects based on the bounding boxes that contain them . The algorithm describes a novel integration of dynamic reference frame differencing and coarse motion estimation to overcome the various occlusion problems encountered in multiple object tracking . Change detection is performed by taking the difference between the current frame and a dynamic reference frame , where the reference frame is adaptively updated over time to account for changes in the background , illumination variations , and the like .
Tracking by detection is a common approach to solving the Multiple Object Tracking problem . In this paper we show how deep metric learning can be used to improve three aspects of tracking by detection .
Such direction-only sensors can be used in applications with the aim of tracking multiple moving objects . One of the reasons why multiple moving object tracking can be challenging is because of the need to deal with the problem of pairing sensors measurements with tracked objects in the presence of clutter ( the data association problem ) . In this paper we propose to approach the problem of multiple object tracking in clutter with direction-only data by setting it on the unit sphere , thus tracking the objects with a Bayesian estimator based on the von Mises-Fisher distribution and probabilistic data association .
Vehicles are detected as moving objects using foreground and background image segmentation . Adjacent pixels in the moving objects image are grouped together and a weight factor based on cluster area , cluster overlapping area and distance between multiple clusters is computed to enable multiple moving object tracking . To ensure real time capabilities , image processing algorithm computation distribution between CPU and GPU is applied .
We make use of a database that involves all previously detected descriptors , forming therefore a discrete search space where the sought solution is a descriptor and its quality is represented by its similarity to the query profile . The approach is evaluated within a multiple object tracking scenario , and a validation process using the normalized cross correlation method to accept or reject the obtained reidentification results is included . The experimental results show promising performances in terms of computation cost as well as reidentification rate .
In this respect , our approach provides a way to obtain a new particle approximation , which is better in a precise information-theoretic sense than that of any of the particle filters alone . The viability of the proposed approach is demonstrated in a multiple object tracking scenario .
Abstract This paper addresses the problem of tracking space-borne objects of interest ( OoIs ) with a satellite-assisted collaborative space surveillance network ( SSN ) . It is formulated as a multiple moving-object tracking problem in a hybrid distributed wireless sensor network ( WSN ) made of both mobile and static nodes . The static nodes may be considered as ground-based sensors with fixed locations , while mobile sensor nodes can be satellites in space .
In our previous work , we proposed an approach to particle filtering which simultaneously adjusts the proposal variance and number of particles for each frame , in order to minimize the tracking distortion for single object tracking . In this paper , we extend our previous work to multiple object video tracking . Under the framework of distributed multiple object tracking , we propose the tracking distortion and use rate distortion theory to derive the optimal particle allocation among multiple targets as well as multiple frames .
In the paper , we achieved the goal in two respects . Regarding the data processing , we studied how to efficiently process tracking data by utilizing the parallel characteristics of iDMA ( integrated direct memory access ) and a DSP core ; and regarding the data storage , we proposed a time-sharing strategy to solve the DSP local memory ( data RAM ) usage issue for multiple tracking objects . In addition , regarding the software design , we propose a new strategy , which includes two levels of parallel computations : the frame-level parallel computations and the tracking object-level parallel computations .
We use the single shot setting . DukeMTMC - reID is a subset of the newly - released multi - target , multi - camera pedestrian tracking dataset . The original dataset contains eight 85 - minute high - resolution videos from eight different cameras .
In recent years , Visual Question Answering ( VisualQA ) has gradually become one of the research hotspots of video understanding , but most of the researches are mainly focused on Image Question Answering ( ImageQA ) , while fewer researches pay attention to Video Question Answering ( VideoQA ) . Inspired by the ImageQA model , we propose a model , which utilizes videos and questions to generate answers .
Region of interest extraction is very important in video analysis and understanding . A simple and fast region of interest extraction method for sport scene images is proposed in this paper .
Functional object recognition in video is an emerging problem for visual surveillance and video understanding problem . By functional objects , we mean objects with specific purpose such as postman and delivery truck , which are defined more by their actions and behaviors than by appearance .
The model is expected to bring the following advantages \2011\202 adequate mixtures of stochastic dynamics and deterministic one can gain representation power of the model , \2012\202 no needs for arbitrary manipulation of data as well as interpretation of them by human , \2013\202 possibility for scaling of the model by incorporating with the scheme of multiple timescales dynamics for extracting temporal hierarchy from the data . The potential impacts by applying the model to sensory-motor sequence learning by robots as well as video image understanding by accumulated learning of the exemplars are discussed .
Human action recognition ( HAR ) has emerged as a core research domain for video understanding and analysis , thus attracting many researchers . Although significant results have been achieved in simple scenarios , HAR is still a challenging task due to issues associated with view independence , occlusion and inter-class variation observed in realistic scenarios .
Multiple object tracking ( MOT ) is an important yet challenging task in video understanding and analysis . Basically , MOT aims to associate detected objects into trajectories based on their temporal relationships .
The approach is able to give good estimates of object dimensions and proposes visual reliability measures for the object dimensions . These measures give a representation of the visibility of the estimated dimension and are principally proposed to aid posterior phases of the video understanding process , as object tracking and event detection . The method obtains the 3D parallelepiped model estimation using a set of 2D moving regions ( obtained in a segmentation phase ) , the perspective matrix transform ( obtained from camera calibration using the pin-hole camera model ) and predefined 3D models of expected objects in the scene .
This approach comprises motion segmentation , object classification , tracking and event learning phases . This work is focused on building the first fundamental blocks allowing a proper management of uncertainty of data in every phase of the video understanding process . The main contributions of the proposed approach are : ( i ) a new algorithm for tracking multiple objects in noisy environments , ( ii ) the utilisation of reliability measures for modelling uncertainty in data and for proper selection of valuable information extracted from noisy data , ( iii ) the improved capability of tracking to manage multiple visual evidence-target associations , ( iv ) the combination of 2D image data with 3D information in a dynamics model governed by reliability measures for proper control of uncertainty in data , and ( v ) a new approach for event recognition through incremental event learning , driven by reliability measures for selecting the most stable and relevant data .
A key task scheduler is designed for the parallelization of computation tasks . The SVM method is used to train models for video understanding purposes . To effectively shorten the training and processing time , the hybrid computing framework is used to train large scale SVM models .
Video interpretation and understanding is one of the long-term research goals in computer vision . Realistic videos such as movies present a variety of challenging machine learning problems , such as action classification/action retrieval , human tracking , human/object interaction classification , etc .
Furthermore , we also present two semantic object segmentation methods , Boundary Neural Fields ( BNFs ) , and Convolutional Random Walk Networks ( RWNs ) , which integrate low-level affinity cues into an object segmentation process . We then shift our focus to video-level understanding , and present a Spatiotemporal Sampling Network ( STSN ) , which can be used for video object detection , and discriminative motion feature learning . Afterwards , we transition into the second subproblem of ` ` seeing to learn ' ' , for which we leverage first-person GoPro cameras that record what people see during a particular activity .
Object detection is an important yet challenging task in video understanding & analysis , where one major challenge lies in the proper balance between two contradictive factors : detection accuracy and detection speed . In this paper , we propose a new adaptive patch-of-interest composition approach for boosting both the accuracy and speed for object detection .
On the other hand , a large number of tools have been proposed in the literature that handle specific segmentation tasks very well . Dynamic segmentation is a possible solution , where the image is split recursively , in a hierarchical fashion , and different tools are used at each step to address specific segmentation tasks . In this work , the segmentation of a high-resolution test image is used as a running example and as a proof of concept of the potential of this approach .
Dynamic segmentation has been neither well specified nor systematically treated in terms of algorithmic development . Vendors have responded to the demand for dynamic segmentation in different ways . Although each vendor has interpreted the problem differently and has invented slightly different solutions , the differences are subtle and difficult to assess without head-to-head comparisons .
An image sensor having a resolution of 1600 by 1200 pixels is used to capture images of symbols in complex backgrounds . Dynamic image segmentation , component labeling and feature extraction was performed on the FPGA . The region of interest was further computed from the extracted features .
The dynamic object segmentation in videos taken from a static camera is a basic technique in many vision surveillance applications . In order to suppress fake objects caused by dynamic cast shadows and reflection images , this paper presents a novel segmentation model with the function of cast shadow and reflection image suppression .
A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues . Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation . key words : video object segmentation , salient object-like proposal , nonlinear regressor , dynamic scene , random forest
Introduction Imagine a scenario where an autonomous robot is wondering in an unknown environment , the location information of foreground targets which can be inferred with an unsupervised method will certainly facilitate , to a large extent , subsequent recognition , tracking as well as visual servo manipulation of the targets . Despite the research efforts in dynamic scene segmentation , designing an algorithm that is robust for a wide variety of dynamic scenes that involve complex natural environments is still an open problem . The bottle neck in dynamic scene segmentation is the foreground object hypothesis stage .
As stated in several works , static time-window length has several drawbacks while adjusting dynamically the window length is more appropriate . However , most of the previous methods for dynamic data segmentation are based on two strong assumptions : the user 's routine does not change and a pre-segmented data set can be provided for learning the time-window size . Yet , these constraints are not always verified .
As stated in several works , static time-window length has several drawbacks while adjusting dynamically the window length is more appropriate . However , each of previous methods for dynamic data segmentation targets only a particular type of application . Hence , there is a need for a general method independent of applications providing high degree of usability .
Each cluster contributes a node along the centerline , which is created by connecting successive nodes until all object voxels are exhausted . While data segmentation is independent of this algorithm , we illustrate the use of the ISODATA method to achieve dynamic ( local ) segmentation . Diameter estimation at each node is calculated using the Rayburst Sampling algorithm , and spurious end nodes caused by surface irregularities are then removed .
In this article , a novel strategy is designed to handle this problem . We leverage both the object detection result and observation information in a Bayesian framework for dynamic region detection . Once the dynamic regions are determined , we discard them and feed the other regions in a state-of-the-art visual simultaneous localization and mapping system for further visual localization .
Now this technique has been used in some GIS software represented by Arc/Info which is based on the " Arc-Node " data model , but the research on using dynamic segmentation in some GIS software with the " entity oriented " data model is seldom . This paper analyses dynamic segmentation data structure in Arc/Info and discusses how to implement the dynamic segmentation in MapInfo .
Dynamic segmentation is an important technique in dynamic analysis , displaying linear features in Geographic Information Systems for transportation ( GIS-T ) and Intelligent Transport Systems ( ITS ) . In this paper , the authors present brief surveys of work in dynamic segmentation development , then present a spatial-temporal dynamic segmentation model . In the conceptual model , components of a dynamic segmentation system merge into a temporal factor .
Retrieving videos by key words requires semantic knowledge of the videos . However , manual video annotation is very costly and time consuming . Most works reported in literatures focus on annotating a video shot with either only one semantic concept or a fixed number of words .
TBased on the user needs and intelligent terminal , trends in the new media industry are analyzed in this paper . Then , the demand of the video industry chain is discussed , and the advantages of telecom operators in the video industry chain is summarized mainly in sales and content distribution channels . The practice of exploring China Mobile , China Unicom , China Telecom is also introduced .
Experimental results show that combining multiple modes of information is more effective in detecting cuts and dissolves . The proposed technique is capable of processing video frames in real time and could be used for the rapid generation of key frames for real-time browsing of video streams and for indexing to support content-based access to video libraries .
Experimental results show that the algorithm is effective in detecting abrupt scene changes ( cuts ) as well as gradual scene changes ( dissolves ) in an MPEG1-coded video stream . The algorithm is capable of processing video frames in real time and could be used for the rapid generation of key frames for real-time browsing of video streams and for indexing to support content-based access to on-line video libraries .
As an example , an error resilient frame-level bit allocation solution is proposed based on the model . Simulation results demonstrate that for lossy video transmission , the proposed method can achieve more consistent and higher overall visual qualities than that of the other nonerror-resilient schemes , which proves the remarkable potential of the new model .
The apparatus is a video playback server which classifies and stores multiple videos by category and plays back a corresponding video in response to a playback request by a smartphone ; the smartphone which connects to the video playback server and requests playback of a video that a user selected ; an uploading terminal which uploads a video to fit in a selected category to the video playback server ; a ranking server which counts the number of playbacks of videos played in the video playback server and determines the rankings of videos in each category ; and a combining server which combines videos corresponding to top rankings by referring to the rankings in each category , and stores the combined video in the video playback server . The combining server combines the videos in order of the rankings , and stores the names of the videos used for video combination and the playback time of each of the videos in a header . The video playback server provides a skip button when the combined video is played back and changes a playback position to a start position of each of the videos when the skip button is selected .
Abnormal event detection from video surveillance is a key issue for social security . At present , the challenge lies in the effective feature extraction of video data . In order to solve the problem , a deep learning method based on convolutional autoencoder was proposed in this paper .
By the methods of theoretic analysis , video watch , mechanics analysis etc , this essay analyzes single hand up shoulder method of shooting , puts great emphasis to analyze the influenced to the clout the holding of rate the ball the method , raises the elbow to stretch the arm , bends the wrist to stir to finger , which propose and pays attention to method of catching , increases the hand the high degree , the bend wrist stir to point and take aim at the integral to increase to shooting percentage and the anti interference of shooting .
This paper describes a 10 bit 30 Msample/s ( MSPS ) CMOS analog-to-digital converter ( ADC ) for high-speed signal processing , especially for subsampling applications , for example digital video broadca ...
The introduction of the sequential and concurrent flow of resources in human cognition and behavior can significantly improve the accuracy and preparation time for intention prediction . This paper introduces a threaded interaction model and applies it to user intention prediction for initial delay reduction in web video access . The proposed technique consists of a candidate selection module , a decision module and a preparation module that prefetches and preloads the web video data before a user 's click action .
The coordination algorithm relies on simple compiler transformations to track the progress of the processes , and performs the adaptive changes locally and asynchronously at each process . Measurements of the runtime overhead of the automatic coordination algorithm for two adaptive applications ( a parallel PDE solver and a distributed video tracking code ) show that the overhead is less than 1 % of execution time for both these codes , even with relatively frequent adaptations , and does not grow significantly with the number of coordinating processes .
As shown in Figure [ reference ] , our method can reconstruct sharper and more accurate images than the state - of - the - art approaches . subsection : Super - resolving video sequences We conduct frame - based SR experiments on two video sequences from with a spatial resolution of pixels .
He received a PhD degree from the University of Cambridge in 2002 , where he worked on 3D reconstruction of architecture from images . His research interests include image - based modeling , automated video surveillance , and image search . Anton van den Hengel is a Professor at the University of Adelaide and the founding Director of The Australian Centre for Visual Technologies ( ACVT ) .
Real-time object tracking and human face detection in cluttered scenes Improving Online Multiple Object tracking with Deep Metric Learning Direction-only tracking of moving objects on the unit sphere via probabilistic data association Real time vehicle detection and tracking on multiple lanes Towards a Novel Reidentification Method Using Metaheuristics Information Fusion Using Particles Intersection Distributed Space Situational Awareness (D-SSA) with a Satellite-assisted Collaborative Space Surveillance Network Optimal Particle Allocation in Particle Filtering for Multiple Object Tracking Research and implementation of multi-object tracking based on vision DSP Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Video question answering by frame attention A Simple and Fast Region of Interest Extraction Approach Based on Computer Vision for Sport Scene Images Content-Based Retrieval of Functional Objects in Video Using Scene Context Studies on a Novel Neuro-dynamic Model for Prediction Learning of Fluctuated Data Streams: Beyond Dichotomy between Probabilistic and Deterministic Models Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) Model for Human Action Recognition Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking FAST AND RELIABLE OBJECT CLASSIFICATION IN VIDEO BASED ON A 3D GENERIC MODEL Uncertainty control for reliable video understanding on complex environments A new parallel video understanding and retrieval system Robust and efficient models for action recognition and localization Embodied Visual Perception Models For Human Behavior Understanding Kill two birds with one stone: Boosting both object detection accuracy and speed with adaptive patch-of-interest composition Dynamic segmentation for image information mining DYNAMIC SEGMENTATION REVISED: A MILEPOINT LINEAR DATA MODEL Real-Time Optical Position Sensing on FPGA A model for dynamic object segmentation with kernel density estimation based on gradient features Nonlinear Regression of Saliency Guided Proposals for Unsupervised Segmentation of Dynamic Scenes Dynamic Scenario Segmentation based on Target Assumption Sort DataSeg: dynamic streaming sensor data segmentation for activity recognition Dynamic Streaming Sensor Data Segmentation for Smart Environment Applications Three-dimensional neuron tracing by voxel scooping Improving Visual Localization Accuracy in Dynamic Environments Based on Dynamic Region Removal An Implementation of Dynamic Segmentation in MapInfo Spatial-Temporal Dynamic Segmentation Model Automatic Annotation and Retrieval for Videos Operators Practice under New Media Trendency Integrated Parsing of Compressed Video Motion-based parsing of compressed video A novel source-channel constant distortion model and its application in error resilient frame-level bit allocation Video combined playback device with video in e-commerce Abnormal Event Detection by Learning Spatiotemporal Features in Videos An Analysis and Probe into Basketball Shooting Skill Design of an ADC for subsampling video applications Gaze-Assisted User Intention Prediction for Initial Delay Reduction in Web Video Access Coordinating adaptations in distributed systems Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
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FUSION ICASSP ICIP (1) 2017 IEEE International Conference on Computer Vision (ICCV) ECCV (1) VCIP ICME IGARSS SAC ICONIP (6) PSIVT VISUAL IW-MMDBMS ICASSP (3) IGTA ICDCS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence
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