Video analysis is a staple of the education research community . For many contemporary education researchers , participation in the video coding process serves as a rite of passage . An important aspect of the work concerns the use of a novel active knowledge representation methodology based on an ontological query language . This representation allows one to pose the problem of video analysis in terms of queries expressed in a visual language incorporating prior hierarchical knowledge of the syntactic and semantic structure of entities , relationships , and events of interest occurring in a video sequence . Perceptual inference then takes place within an ontological domain defined by the structure of the problem and the current goal set . We implemented media lifelog system with highlighting system with image analysis , video analysis and audio segmentation modules . Image analysis module has image classification , saliency region detection , face detection and facial expression recognition process . This paper proposed a deep learning model for automatic evaluation of academic engagement based on video data analysis . A coding system based on the BROMP standard for behavioral , emotional , and cognitive states was defined to code typical videos in an autonomous learning environment . Recently , there has been increased need and interest in ” video analysis ” which is analysis of video in sequence to determine relatively moving objects like vehicles and different behaviors of people . for eg . Due to long distances between individual student teachers and the limited amount of time for reflection during face-to-face sessions , analysis and reflection processes can not be realized in blended learning arrangements with the same means available in traditional face-to-face teacher education . The project " v-share " therefore develops the methodological concept and technical support for video-based analysis and reflection on teaching experiences in ( virtual ) groups . The internet-based tool allows student teachers in blended learning arrangements to share videos of their own and their fellow student teachers ' teaching lessons and to select sequences for joint online analysis , annotation and reflection . The great penetration of Web sites raises a number of challenges for usability evaluators . Video-based analysis can be rather expensive and may provide limited results . In this article , we discuss what information can be provided by automatic tools able to process the information contained in browser logs and task models . One of the most important tools for animal behavior and animal welfare scientists is video-based analysis . Despite the fact that automatic analysis system exists for many species , field of automated video-based behavior analysis does n't have an effective tool for higher animals , as dogs and therefore scientists have to use manual analysis . This new business mode has enriched communication among users by sending users ' feelings through danmus and sharing these danmus on time synchronized videos . Can danmu communication be helpful for better user behavior modeling or video analyzing ? To this question , in this paper , preliminary attempts are made on analysis of users and videos by introducing a Danmu dataset which is collected from a real-world danmu-enabled video sharing platform . People counting in extremely dense crowds is a challenging problem due to severe occlusions , few pixels per head , cluttered environments , and skewed camera perspectives . In this paper , we present a novel algorithm for people counting in highly dense crowd images . This approach extracts the knowledge from images and uses it to perform real-time reasoning according to the contextual information , events of interest and logic rules . The reasoning with image knowledge allows protecting the privacy of the users , to overcome some problems of computer vision such as occlusion and missed detections and to offer services such as people guidance and people counting . This approach is the first step to develop an " all-seeing " smart building that can automatically react according to its evolving information . Motivated by the success of applying “ ambiguous labelling ” to age estimation problem , we also manage to employ this strategy to the people counting problem . We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem . Also , by applying “ ambiguous labelling ” , we are able to augment the size of training dataset , which is a desirable property when applying to deep learning model . In this paper , we present a system that operates in a novel application context for these devices , in troublesome scenarios where illumination conditions can suffer sudden changes . We focus on the people counting problem with re-identification and trajectory analysis . Automatic people counting offers different application contexts related to security , safety , energy saving or fraud control . People count is one of the tedious tasks if it involves human intervene . As an alternative , automation of the process is done using image processing as it provides high accuracy . Among the source separation approaches , the independent component analysis ( ICA ) is the most prevalent , being involved in different areas of signal separation applications . Despite the improvements being achieved in foreground extraction , the sudden luminance variations and background movements adversely impact the results of techniques in this regard . In this paper , a novel structure called HSIC_ICA is introduced to address the mentioned problem using a modified version of the ICA algorithm which , leverages the Hilbert-Schmidt Independence Criterion ( HSIC ) instead of the common objective functions . We believe that if additional information is provided to users in order to guide them effectively , the amount of interaction required can be reduced . Consequently , in this paper we propose an efficient foreground extraction algorithm , which utilizes depth information from RGB-D sensors such as Microsoft Kinect and offers users guidance in the foreground extraction process . Our approach can be applied as a pre-processing step for interactive and energy-minimization-based segmentation approaches . Based on the results of trajectory tracking , algorithms are designed for each type of anomaly . Furthermore , we improve the frame differential using the double fusion method during the foreground extraction process , which is especially crucial for detecting the abandoned objects . Feature representations of spatial and temporal . Considering those disadvantages above , the background updating algorithm which be advanced in the research can reduce the effect by shadow effectively by doing the calculation of H chrominance component on HSI space , achieving a lasting update automatically by operating the frequency statistics after every sampling . Applying the method of combining edge detection of morphologic and difference of background together to the target detection , a method of foreground area extraction based on the edge information has been brought forward in this research . The method of morphologic edge detection algorithm has a good restrain on noises while doing the edge detection . The foreground extraction of video is derived from frame difference and background subtraction . These methods have always relied on the temporal consistency of successive video frames . Compared with FCM and original level set methods , some remarkable characteristics and better performance have been demonstrated . In this paper , we mainly concern with the foreground extraction problem , wherein some extended works are also reported including detailed analysis on the convergence of FCMLS , multiregional FCMLS . Our research results indicate that FCMLS has good performance and is very worthful for foreground image extraction . Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations , camera jitters , and intermittent object motions . It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios ; unlike case-specific methods , which normally fail for their nonconsidered scenarios . In this paper , we propose a system to simulate background blurring function , which consists of two main steps . In foreground extraction step , firstly , we select meanshift based segmentation method to get over-segmented regions . Then we extract both texture and color features for each region . The video frame foreground extraction and object prediction with occlusion is one of the major challenges in real time . Researchers are working on some novel foreground extraction techniques facing occlusion like major challenge in foreground feature extraction . In this paper a new parameter centroid matching has been proposed to extract foreground using k-mean clustering . Approaches such as used region - level cues to infer the foreground segmentation mask and to discard the noisy visual information in the background . Chai et al . showed that jointly learning part localization and foreground segmentation together can be beneficial for fine - grained categorization . Similar to most previous part - based approaches , these efforts require the ground truth bounding box to initialize the segmentation seed . In this work , we carefully designed a lab-study to examine the impact of duration length on user 's time perception in the context of search . Experimental results show that Vierordt 's law is consistently observed in Web search environment . This work could help us to correct the estimation of users ' perceived time and provide insights about the mechanism of satisfaction . User intent in Web search environment is defined as user 's information need , and believed to be found by analyzing past data such as queries , click histories and user profiles . However , users may have different intents even in the same queries . At the level of ranked lists of documents , we propose several novel prediction models that capture different aspects of the retrieval process that have a major impact on retrieval effectiveness . These techniques make performance prediction both effective and efficient in various retrieval settings including a Web search environment . As an application , we also provide a framework to address the problem of query expansion prediction . But there are fundamental limits on the capabilities of systems that do not retain some information about the world . In particular , a representation of the local space , i.e. , spatial memory , is required to efficiently perform search and avoidance tasks in a realistic environment . Given that it is intractable to maintain a completely current and accurate model of a dynamic world , the proposed research will investigate the extent to which a world model is necessary for accomplishing particular tasks in a dynamic three-dimensional world , and the form of that model . Finding sought objects requires the brain to combine visual and target signals to determine when a target is in view . To investigate how the brain implements these computations , we recorded neural responses in inferotemporal cortex ( IT ) and perirhinal cortex ( PRH ) as macaque monkeys performed a delayed-match-to-sample target search task . Our data suggest that visual and target signals were combined within or before IT in the ventral visual pathway and then passed onto PRH , where they were reformatted into a more explicit target match signal over ∼10–15 ms . Bringing new and quality pages to users is important because most users want the latest information . Research publication search has exactly the same problem . This paper studies the temporal dimension of search in the context of research publication search . New pages , which may be of high quality , have few or no in-links and are left behind . Research publication search has the same problem . If we use the PageRank or HITS algorithm , those older or classic papers are ranked high due to the large number of citations that they received in the past . Abstract The volume of natural language text data has been rapidly increasing over the past two decades , due to factors such as the growth of the Web , the low cost associated with publishing , and the progress on the digitization of printed texts . This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap . This book explores two interrelated and important areas of overlap : ( 1 ) managing natural language data and ( 2 ) developing natural language interfaces to databases . The volume of natural language text data has been rapidly increasing over the past two decades , due to factors such as the growth of the Web , the low cost associated to publishing and the progress on the digitization of printed texts . This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap . This tutorial explores two more relevant areas of overlap to the database community : ( 1 ) managing natural language text data in a relational database , and ( 2 ) developing natural language interfaces to databases . In this paper , we propose an adaptation of the ACT-R cognitive architecture that incorporates a notion of elapsed time for the current goal and uses time to determine when to switch away from the current task . We demonstrate the usefulness of this mechanism in an application to a dynamic , time-critical dual search task , showing how an ACT-R model can account for various aspects of human subjects ' multitasking behavior . Planning , controlling and coordinating search and rescue operations is complex and time is crucial for survivors who must be found quickly . The search planning phase is especially important when the location of the incident is unknown . We propose , implement , solve , and evaluate mathematical models for the multiple rectangular search area problem . However , its major drawback is that when sequential search is used to find the neighbors , it involves high computational cost . Speeding-up k-NN search is still an active research field . Hwang and Cho have recently proposed an adaptive cluster-based method for fast Nearest Neighbor searching . This will develop the application of reinforcement learning and provide a new idea for Spider . From the experimental results , the conclusion is that the performance will be improved a lot with this technology in spider search by the experimental analysis . 1 . SYSTEM OVERVIEW People search is an important problem , because users often require to identify relevant people rather than documents . For instance , in an academic research setting , researchers may seek other researchers with complementary skills for a given project . While still approximate , this strategy can recover higher scoring sequences that are often also better in terms of our final evaluation metric . This process is commonly know as Beam Search . The downside of using beam search is that it significantly slows down the generation process . We use a beam of width 5 . We divide the log - likelihoods of the final hypothesis in beam search by their length |y|. subsection : Beam search ranking criterion The modified log - probability ranking criterion we used in beam search for translation experiments is : where is a length normalization parameter Wu2016 , is the th output character , and is a “ target length ” equal to the source sentence length plus five characters . This reduces at to ordinary beam search with probabilities : and at to beam search with probabilities normalized by length ( up to the target length ) : Conveniently , this ranking criterion can be computed at intermediate beam - search timesteps , obviating the need to apply a separate reranking on complete hypotheses . R - CNN and its variants use region proposals instead of sliding windows to find objects in images . Selective Search generates potential bounding boxes , a convolutional network extracts features , an SVM scores the boxes , a linear model adjusts the bounding boxes , and non - max suppression eliminates duplicate detections . Each stage of this complex pipeline must be precisely tuned independently and the resulting system is very slow , taking more than 40 seconds per image at test time . For each image , we use 2k proposals for all the methods . First , two popular methods that work well on PASCAL VOC , Selective Search and Edge Boxes , do not perform well on KITTI , mainly because objects in KITTI exhibit more significant scale variation , occlusion and truncation . It is challenging for a bottom - up proposal method to achieve high recall under a small budget ( i.e , 2k boxes per image ) . We thus exploit a circular queue to store their features also for comparison . This is another advantage brought by the person search problem setting . The proposed parameter - free OIM loss converges much faster and better than the Softmax loss in our experiments . Image denoising is crucial to improve the quality of image visual , their effects , and/ or facilitating image analysis and processing . Image noise can appear in many imaging applications such as remote sensing surveillance and assistant of medical surgery . Noises are often introduced during the image acquisition process when the image acquisition sensor is being interfered . Then the contact area and the stress including the third condyle were calculated individually using finite element ( FE ) analysis . FINDINGS The motion patterns were similar in the simulation program and the fluoroscopic surveillance . Our developed model could nearly estimate the individual in vivo knee kinematics . Increased communication , speed in transactions , accuracy in administrative tasks and collaboration amongst team members are the obvious advantages . The implementation of IT has gone much , much beyond - Elearning , virtual classrooms , IP Cameras enabling archives of surveillance ; global connectivity of classrooms in case of guest lectures are only some of the initiatives . This study traces evolution and implementation of IT . Object detection is used to detect the object present in the video and to find the exact location of that object . The object tracking can be applied in various fields that include video surveillance , robot vision , traffic monitoring , automated civil or military surveillance system , traffic monitoring , human-computer interaction , vehicle navigation , biomedical image analysis , medical imaging and much more . The object tracking algorithm requires tracking the object in each frame of the video . In this paper , we present the design of the proposed framework and evaluate a prototype implementation . The results of this evaluation show that this implementation is efficient and can successfully perform fail over handling , making it suitable for distributing surveillance applications . Dynamic Bayesian networks are a promising approach to automatic video content analysis which allows statistical inference and learning to be combined with domain knowledge . When the particle filter ( PF ) is used for approximate inference , video data can often be classified in real-time ( supporting e.g. on-line automatic video surveillance ) . Unfortunately , the limited processing resources available on a typical host restricts the complexity , accuracy , and frame rate of PF classification tasks . In the spatial target surveillance and astronomical observation applications , image matching processing is the key procedure for the multi-temporal starry images or the multi-channel starry images acquired by different imaging sensors . However , the starry images obtained often have low Signal-to-Noise Ratios ( SNR ) , the light intensities of the target stars or spacecrafts in them are vulnerable to background interferences , such as the atmospheric turbulence and the night clouds , etc . , and become dim and instable . All the components are shielded inside a shell ; therefore it is possible to make this robot to be waterproof . One of the applications of this spherical robot is an industrial piping surveillance robot . The robot considered in this study is semi-autonomous where a user moves the robot through user interface installed in an android . Marked-hole 's area , perimeter and moment invariants are extracted as cluster features . The auto-scoring software , programmed by Visual C++ , has successfully solved the recognition of off-target and overlapped holes through alarming surveillance and bullet tacking programs . The experimental results show that , when the target is distorted obviously , the system can recognize the overlapped holes on real time and also clusters random shape holes on the target correctly . Interestingly , information generation influences the demand for the stock , but information content and information presentation drive the willingness to pay . Our results are highly relevant for Internet users , software vendors , and market surveillance authorities , as a deep understanding of such information‐based manipulations is necessary to develop appropriate countermeasures .
Video analysis is a staple of the education research community . For many contemporary education researchers , participation in the video coding process serves as a rite of passage .
An important aspect of the work concerns the use of a novel active knowledge representation methodology based on an ontological query language . This representation allows one to pose the problem of video analysis in terms of queries expressed in a visual language incorporating prior hierarchical knowledge of the syntactic and semantic structure of entities , relationships , and events of interest occurring in a video sequence . Perceptual inference then takes place within an ontological domain defined by the structure of the problem and the current goal set .
We implemented media lifelog system with highlighting system with image analysis , video analysis and audio segmentation modules . Image analysis module has image classification , saliency region detection , face detection and facial expression recognition process .
This paper proposed a deep learning model for automatic evaluation of academic engagement based on video data analysis . A coding system based on the BROMP standard for behavioral , emotional , and cognitive states was defined to code typical videos in an autonomous learning environment .
Recently , there has been increased need and interest in ” video analysis ” which is analysis of video in sequence to determine relatively moving objects like vehicles and different behaviors of people . for eg .
Due to long distances between individual student teachers and the limited amount of time for reflection during face-to-face sessions , analysis and reflection processes can not be realized in blended learning arrangements with the same means available in traditional face-to-face teacher education . The project " v-share " therefore develops the methodological concept and technical support for video-based analysis and reflection on teaching experiences in ( virtual ) groups . The internet-based tool allows student teachers in blended learning arrangements to share videos of their own and their fellow student teachers ' teaching lessons and to select sequences for joint online analysis , annotation and reflection .
The great penetration of Web sites raises a number of challenges for usability evaluators . Video-based analysis can be rather expensive and may provide limited results . In this article , we discuss what information can be provided by automatic tools able to process the information contained in browser logs and task models .
One of the most important tools for animal behavior and animal welfare scientists is video-based analysis . Despite the fact that automatic analysis system exists for many species , field of automated video-based behavior analysis does n't have an effective tool for higher animals , as dogs and therefore scientists have to use manual analysis .
This new business mode has enriched communication among users by sending users ' feelings through danmus and sharing these danmus on time synchronized videos . Can danmu communication be helpful for better user behavior modeling or video analyzing ? To this question , in this paper , preliminary attempts are made on analysis of users and videos by introducing a Danmu dataset which is collected from a real-world danmu-enabled video sharing platform .
People counting in extremely dense crowds is a challenging problem due to severe occlusions , few pixels per head , cluttered environments , and skewed camera perspectives . In this paper , we present a novel algorithm for people counting in highly dense crowd images .
This approach extracts the knowledge from images and uses it to perform real-time reasoning according to the contextual information , events of interest and logic rules . The reasoning with image knowledge allows protecting the privacy of the users , to overcome some problems of computer vision such as occlusion and missed detections and to offer services such as people guidance and people counting . This approach is the first step to develop an " all-seeing " smart building that can automatically react according to its evolving information .
Motivated by the success of applying “ ambiguous labelling ” to age estimation problem , we also manage to employ this strategy to the people counting problem . We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem . Also , by applying “ ambiguous labelling ” , we are able to augment the size of training dataset , which is a desirable property when applying to deep learning model .
In this paper , we present a system that operates in a novel application context for these devices , in troublesome scenarios where illumination conditions can suffer sudden changes . We focus on the people counting problem with re-identification and trajectory analysis . Automatic people counting offers different application contexts related to security , safety , energy saving or fraud control .
People count is one of the tedious tasks if it involves human intervene . As an alternative , automation of the process is done using image processing as it provides high accuracy .
Among the source separation approaches , the independent component analysis ( ICA ) is the most prevalent , being involved in different areas of signal separation applications . Despite the improvements being achieved in foreground extraction , the sudden luminance variations and background movements adversely impact the results of techniques in this regard . In this paper , a novel structure called HSIC_ICA is introduced to address the mentioned problem using a modified version of the ICA algorithm which , leverages the Hilbert-Schmidt Independence Criterion ( HSIC ) instead of the common objective functions .
We believe that if additional information is provided to users in order to guide them effectively , the amount of interaction required can be reduced . Consequently , in this paper we propose an efficient foreground extraction algorithm , which utilizes depth information from RGB-D sensors such as Microsoft Kinect and offers users guidance in the foreground extraction process . Our approach can be applied as a pre-processing step for interactive and energy-minimization-based segmentation approaches .
Based on the results of trajectory tracking , algorithms are designed for each type of anomaly . Furthermore , we improve the frame differential using the double fusion method during the foreground extraction process , which is especially crucial for detecting the abandoned objects . Feature representations of spatial and temporal .
Considering those disadvantages above , the background updating algorithm which be advanced in the research can reduce the effect by shadow effectively by doing the calculation of H chrominance component on HSI space , achieving a lasting update automatically by operating the frequency statistics after every sampling . Applying the method of combining edge detection of morphologic and difference of background together to the target detection , a method of foreground area extraction based on the edge information has been brought forward in this research . The method of morphologic edge detection algorithm has a good restrain on noises while doing the edge detection .
The foreground extraction of video is derived from frame difference and background subtraction . These methods have always relied on the temporal consistency of successive video frames .
Compared with FCM and original level set methods , some remarkable characteristics and better performance have been demonstrated . In this paper , we mainly concern with the foreground extraction problem , wherein some extended works are also reported including detailed analysis on the convergence of FCMLS , multiregional FCMLS . Our research results indicate that FCMLS has good performance and is very worthful for foreground image extraction .
Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations , camera jitters , and intermittent object motions . It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios ; unlike case-specific methods , which normally fail for their nonconsidered scenarios .
In this paper , we propose a system to simulate background blurring function , which consists of two main steps . In foreground extraction step , firstly , we select meanshift based segmentation method to get over-segmented regions . Then we extract both texture and color features for each region .
The video frame foreground extraction and object prediction with occlusion is one of the major challenges in real time . Researchers are working on some novel foreground extraction techniques facing occlusion like major challenge in foreground feature extraction . In this paper a new parameter centroid matching has been proposed to extract foreground using k-mean clustering .
Approaches such as used region - level cues to infer the foreground segmentation mask and to discard the noisy visual information in the background . Chai et al . showed that jointly learning part localization and foreground segmentation together can be beneficial for fine - grained categorization . Similar to most previous part - based approaches , these efforts require the ground truth bounding box to initialize the segmentation seed .
In this work , we carefully designed a lab-study to examine the impact of duration length on user 's time perception in the context of search . Experimental results show that Vierordt 's law is consistently observed in Web search environment . This work could help us to correct the estimation of users ' perceived time and provide insights about the mechanism of satisfaction .
User intent in Web search environment is defined as user 's information need , and believed to be found by analyzing past data such as queries , click histories and user profiles . However , users may have different intents even in the same queries .
At the level of ranked lists of documents , we propose several novel prediction models that capture different aspects of the retrieval process that have a major impact on retrieval effectiveness . These techniques make performance prediction both effective and efficient in various retrieval settings including a Web search environment . As an application , we also provide a framework to address the problem of query expansion prediction .
But there are fundamental limits on the capabilities of systems that do not retain some information about the world . In particular , a representation of the local space , i.e. , spatial memory , is required to efficiently perform search and avoidance tasks in a realistic environment . Given that it is intractable to maintain a completely current and accurate model of a dynamic world , the proposed research will investigate the extent to which a world model is necessary for accomplishing particular tasks in a dynamic three-dimensional world , and the form of that model .
Finding sought objects requires the brain to combine visual and target signals to determine when a target is in view . To investigate how the brain implements these computations , we recorded neural responses in inferotemporal cortex ( IT ) and perirhinal cortex ( PRH ) as macaque monkeys performed a delayed-match-to-sample target search task . Our data suggest that visual and target signals were combined within or before IT in the ventral visual pathway and then passed onto PRH , where they were reformatted into a more explicit target match signal over ∼10–15 ms .
Bringing new and quality pages to users is important because most users want the latest information . Research publication search has exactly the same problem . This paper studies the temporal dimension of search in the context of research publication search .
New pages , which may be of high quality , have few or no in-links and are left behind . Research publication search has the same problem . If we use the PageRank or HITS algorithm , those older or classic papers are ranked high due to the large number of citations that they received in the past .
Abstract The volume of natural language text data has been rapidly increasing over the past two decades , due to factors such as the growth of the Web , the low cost associated with publishing , and the progress on the digitization of printed texts . This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap . This book explores two interrelated and important areas of overlap : ( 1 ) managing natural language data and ( 2 ) developing natural language interfaces to databases .
The volume of natural language text data has been rapidly increasing over the past two decades , due to factors such as the growth of the Web , the low cost associated to publishing and the progress on the digitization of printed texts . This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap . This tutorial explores two more relevant areas of overlap to the database community : ( 1 ) managing natural language text data in a relational database , and ( 2 ) developing natural language interfaces to databases .
In this paper , we propose an adaptation of the ACT-R cognitive architecture that incorporates a notion of elapsed time for the current goal and uses time to determine when to switch away from the current task . We demonstrate the usefulness of this mechanism in an application to a dynamic , time-critical dual search task , showing how an ACT-R model can account for various aspects of human subjects ' multitasking behavior .
Planning , controlling and coordinating search and rescue operations is complex and time is crucial for survivors who must be found quickly . The search planning phase is especially important when the location of the incident is unknown . We propose , implement , solve , and evaluate mathematical models for the multiple rectangular search area problem .
However , its major drawback is that when sequential search is used to find the neighbors , it involves high computational cost . Speeding-up k-NN search is still an active research field . Hwang and Cho have recently proposed an adaptive cluster-based method for fast Nearest Neighbor searching .
This will develop the application of reinforcement learning and provide a new idea for Spider . From the experimental results , the conclusion is that the performance will be improved a lot with this technology in spider search by the experimental analysis .
1 . SYSTEM OVERVIEW People search is an important problem , because users often require to identify relevant people rather than documents . For instance , in an academic research setting , researchers may seek other researchers with complementary skills for a given project .
While still approximate , this strategy can recover higher scoring sequences that are often also better in terms of our final evaluation metric . This process is commonly know as Beam Search . The downside of using beam search is that it significantly slows down the generation process .
We use a beam of width 5 . We divide the log - likelihoods of the final hypothesis in beam search by their length |y|.
subsection : Beam search ranking criterion The modified log - probability ranking criterion we used in beam search for translation experiments is : where is a length normalization parameter Wu2016 , is the th output character , and is a “ target length ” equal to the source sentence length plus five characters . This reduces at to ordinary beam search with probabilities : and at to beam search with probabilities normalized by length ( up to the target length ) : Conveniently , this ranking criterion can be computed at intermediate beam - search timesteps , obviating the need to apply a separate reranking on complete hypotheses .
R - CNN and its variants use region proposals instead of sliding windows to find objects in images . Selective Search generates potential bounding boxes , a convolutional network extracts features , an SVM scores the boxes , a linear model adjusts the bounding boxes , and non - max suppression eliminates duplicate detections . Each stage of this complex pipeline must be precisely tuned independently and the resulting system is very slow , taking more than 40 seconds per image at test time .
For each image , we use 2k proposals for all the methods . First , two popular methods that work well on PASCAL VOC , Selective Search and Edge Boxes , do not perform well on KITTI , mainly because objects in KITTI exhibit more significant scale variation , occlusion and truncation . It is challenging for a bottom - up proposal method to achieve high recall under a small budget ( i.e , 2k boxes per image ) .
We thus exploit a circular queue to store their features also for comparison . This is another advantage brought by the person search problem setting . The proposed parameter - free OIM loss converges much faster and better than the Softmax loss in our experiments .
Image denoising is crucial to improve the quality of image visual , their effects , and/ or facilitating image analysis and processing . Image noise can appear in many imaging applications such as remote sensing surveillance and assistant of medical surgery . Noises are often introduced during the image acquisition process when the image acquisition sensor is being interfered .
Then the contact area and the stress including the third condyle were calculated individually using finite element ( FE ) analysis . FINDINGS The motion patterns were similar in the simulation program and the fluoroscopic surveillance . Our developed model could nearly estimate the individual in vivo knee kinematics .
Increased communication , speed in transactions , accuracy in administrative tasks and collaboration amongst team members are the obvious advantages . The implementation of IT has gone much , much beyond - Elearning , virtual classrooms , IP Cameras enabling archives of surveillance ; global connectivity of classrooms in case of guest lectures are only some of the initiatives . This study traces evolution and implementation of IT .
Object detection is used to detect the object present in the video and to find the exact location of that object . The object tracking can be applied in various fields that include video surveillance , robot vision , traffic monitoring , automated civil or military surveillance system , traffic monitoring , human-computer interaction , vehicle navigation , biomedical image analysis , medical imaging and much more . The object tracking algorithm requires tracking the object in each frame of the video .
In this paper , we present the design of the proposed framework and evaluate a prototype implementation . The results of this evaluation show that this implementation is efficient and can successfully perform fail over handling , making it suitable for distributing surveillance applications .
Dynamic Bayesian networks are a promising approach to automatic video content analysis which allows statistical inference and learning to be combined with domain knowledge . When the particle filter ( PF ) is used for approximate inference , video data can often be classified in real-time ( supporting e.g. on-line automatic video surveillance ) . Unfortunately , the limited processing resources available on a typical host restricts the complexity , accuracy , and frame rate of PF classification tasks .
In the spatial target surveillance and astronomical observation applications , image matching processing is the key procedure for the multi-temporal starry images or the multi-channel starry images acquired by different imaging sensors . However , the starry images obtained often have low Signal-to-Noise Ratios ( SNR ) , the light intensities of the target stars or spacecrafts in them are vulnerable to background interferences , such as the atmospheric turbulence and the night clouds , etc . , and become dim and instable .
All the components are shielded inside a shell ; therefore it is possible to make this robot to be waterproof . One of the applications of this spherical robot is an industrial piping surveillance robot . The robot considered in this study is semi-autonomous where a user moves the robot through user interface installed in an android .
Marked-hole 's area , perimeter and moment invariants are extracted as cluster features . The auto-scoring software , programmed by Visual C++ , has successfully solved the recognition of off-target and overlapped holes through alarming surveillance and bullet tacking programs . The experimental results show that , when the target is distorted obviously , the system can recognize the overlapped holes on real time and also clusters random shape holes on the target correctly .
Interestingly , information generation influences the demand for the stock , but information content and information presentation drive the willingness to pay . Our results are highly relevant for Internet users , software vendors , and market surveillance authorities , as a deep understanding of such information‐based manipulations is necessary to develop appropriate countermeasures .
(Dis)engagement matters: identifying efficacious learning practices with multimodal learning analytics A Self-Referential Perceptual Inference Framework for Video Interpretation Image understanding for global lifelog media cloud A deep learning model for automatic evaluation of academic engagement To Detect and Track Moving Object for Surveillance V-share - video-based analysis and reflection of teaching experiences in (virtual) groups Tools for remote usability evaluation of Web applications through browser logs and task models Software Architecture of the Automated Animal Behavior Analysis System Understanding the Users and Videos by Mining a Novel Danmu Dataset People Counting in Dense Crowd Images Using Sparse Head Detections Know Beyond Seeing: Combining Computer Vision with Semantic Reasoning A Classification-Regression Deep Learning Model for People Counting People counting with re-identification using depth cameras An Enhanced Viola–Jones Face Detection Method with Skin Mapping & Segmentation Foreground Extraction Using Hilbert-Schmidt Independence Criterion and Particle Swarm Optimization Independent Component Analysis Efficient foreground extraction using RGB-D imaging Anomaly detection by exploiting the tracking trajectory in surveillance videos Research on Vehicle Detection Method Based on Video Image Deep Video Foreground Target Extraction with Complex Scenes Some Remarks on FCMLS with its Application to Foreground Extraction SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos Make Your Picture More Attractive - An Interactive System for Image Blur A centroid matching approach for real time foreground segmentation and occlusion handling Part-Based R-CNNs for Fine-Grained Category Detection Investigating Users' Time Perception during Web Search Intent feature discovery using Q&A corpus and web data Retrieval performance prediction and document quality Perception and action in a dynamic three-dimensional world Dynamic Target Match Signals in Perirhinal Cortex Can Be Explained by Instantaneous Computations That Act on Dynamic Input from Inferotemporal Cortex On the temporal dimension of search Adding the temporal dimension to search - a case study in publication search Natural Language Data Management and Interfaces Natural Language Data Management and Interfaces: Recent Development and Open Challenges Deciding when to switch tasks in time-critical multitasking Decision Support for Search and Rescue Response Planning An Extensive Experimental Study on the Cluster-based Reference Set Reduction for speeding-up the k-NN Classifier The Research on the Spider of the Domain-Specific Search Engines Based on the Reinforcement Learning Expertise search in academia using facets Sequence Level Training with Recurrent Neural Networks Convolutional Sequence to Sequence Learning Quasi-Recurrent Neural Networks You Only Look Once: Unified, Real-Time Object Detection Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection Joint Detection and Identification Feature Learning for Person Search A Comparison on Sparse Coding and Moran's I Method for Image Denoising How exactly can computer simulation predict the kinematics and contact status after TKA? Examination in individualized models. Information Technology: The Change Agent for Paradigm of Education - with Special Reference to Indian Conditions Overview of Object Detection and Tracking based on Block Matching Techniques ViFramework: A framework for networked video streaming components Techniques for Parallel Execution of the Particle Filter Starry image matching method based on the description of multi-scale geometric invariant features Motion Control Analysis of a Spherical Robot as a Surveillance Robot Design of automatic target-scoring system of shooting game based on computer vision The economics of stock touting during Internet‐based pump and dump campaigns
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LAK ICVS L@S CSCL ICSC IntelliSys (1) ECCV CHIIR ICUIMC WWW (Alternate Track Papers &amp; Posters) Web Intelligence SIGMOD Conference ICCM ISCRAM SIGIR ICLR ICML ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) RACS RICE SCIA
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