Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425356
T. Shan, Shaokang Chen, Conrad Sanderson, B. Lovell
In recent years, the use of Intelligent Closed-Circuit Television (ICCTV) for crime prevention and detection has attracted significant attention. Existing face recognition systems require passport-quality photos to achieve good performance. However, use of CCTV images is much more problematic due to large variations in illumination, facial expressions and pose angle. In this paper we propose a pose variability compensation technique, which synthesizes realistic frontal face images from non-frontal views. It is based on modelling the face via Active Appearance Models and detecting the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. Experiments on the FERET dataset show up to 6 fold performance improvements. Finally, in addition to implementation and scalability challenges, we discuss issues related to on-going real life trials in public spaces using existing surveillance hardware.
{"title":"Towards robust face recognition for Intelligent-CCTV based surveillance using one gallery image","authors":"T. Shan, Shaokang Chen, Conrad Sanderson, B. Lovell","doi":"10.1109/AVSS.2007.4425356","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425356","url":null,"abstract":"In recent years, the use of Intelligent Closed-Circuit Television (ICCTV) for crime prevention and detection has attracted significant attention. Existing face recognition systems require passport-quality photos to achieve good performance. However, use of CCTV images is much more problematic due to large variations in illumination, facial expressions and pose angle. In this paper we propose a pose variability compensation technique, which synthesizes realistic frontal face images from non-frontal views. It is based on modelling the face via Active Appearance Models and detecting the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. Experiments on the FERET dataset show up to 6 fold performance improvements. Finally, in addition to implementation and scalability challenges, we discuss issues related to on-going real life trials in public spaces using existing surveillance hardware.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425344
Zafer Arican, P. Frossard
This paper addresses the problem of dense estimation of disparities between omnidirectional images, in a spherical framework. Omnidirectional imaging certainly represents important advantages for the representation and processing of the plenoptic function in 3D scenes for applications in localization, or depth estimation for example. In this context, we propose to perform disparity estimation directly in a spherical framework, in order to avoid discrepancies due to inexact projections of omnidirectional images onto planes. We first perform rectification of the omnidirectional images in the spherical domain. Then we develop a global energy minimization algorithm based on the graph-cut algorithm, in order to perform disparity estimation on the sphere. Experimental results show that the proposed algorithm outperforms typical methods as the ones based on block matching, for both a simple synthetic scene, and complex natural scenes. The proposed method shows promising performances for dense disparity estimation and can be extended efficiently to networks of several camera sensors.
{"title":"Dense disparity estimation from omnidirectional images","authors":"Zafer Arican, P. Frossard","doi":"10.1109/AVSS.2007.4425344","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425344","url":null,"abstract":"This paper addresses the problem of dense estimation of disparities between omnidirectional images, in a spherical framework. Omnidirectional imaging certainly represents important advantages for the representation and processing of the plenoptic function in 3D scenes for applications in localization, or depth estimation for example. In this context, we propose to perform disparity estimation directly in a spherical framework, in order to avoid discrepancies due to inexact projections of omnidirectional images onto planes. We first perform rectification of the omnidirectional images in the spherical domain. Then we develop a global energy minimization algorithm based on the graph-cut algorithm, in order to perform disparity estimation on the sphere. Experimental results show that the proposed algorithm outperforms typical methods as the ones based on block matching, for both a simple synthetic scene, and complex natural scenes. The proposed method shows promising performances for dense disparity estimation and can be extended efficiently to networks of several camera sensors.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126869932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425371
A. Aksay, A. Temi̇zel, A. Cetin
It is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or de-focusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these.
{"title":"Camera tamper detection using wavelet analysis for video surveillance","authors":"A. Aksay, A. Temi̇zel, A. Cetin","doi":"10.1109/AVSS.2007.4425371","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425371","url":null,"abstract":"It is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or de-focusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115090872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425283
G. Passalis, I. Kakadiaris, T. Theoharis, G. Toderici, Theodoros Papaioannou
Three-dimensional data are increasingly being used for biometric purposes as they offer resilience to problems common in two-dimensional data. They have been successfully applied to face recognition and more recently to ear recognition. However, real-life biometric applications require algorithms that are both robust and efficient so that they scale well with the size of the databases. A novel ear recognition method is presented that uses a generic annotated ear model to register and fit each ear dataset. Then a compact biometric signature is extracted that retains 3D information. The proposed method is evaluated using the largest publicly available 3D ear database appended with our own database, resulting in a database containing data from multiple 3D sensor types. Using this database it is shown that the proposed method is not only robust, accurate and sensor invariant but also extremely efficient, thus making it suitable for real-life biometric applications.
{"title":"Towards fast 3D ear recognition for real-life biometric applications","authors":"G. Passalis, I. Kakadiaris, T. Theoharis, G. Toderici, Theodoros Papaioannou","doi":"10.1109/AVSS.2007.4425283","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425283","url":null,"abstract":"Three-dimensional data are increasingly being used for biometric purposes as they offer resilience to problems common in two-dimensional data. They have been successfully applied to face recognition and more recently to ear recognition. However, real-life biometric applications require algorithms that are both robust and efficient so that they scale well with the size of the databases. A novel ear recognition method is presented that uses a generic annotated ear model to register and fit each ear dataset. Then a compact biometric signature is extracted that retains 3D information. The proposed method is evaluated using the largest publicly available 3D ear database appended with our own database, resulting in a database containing data from multiple 3D sensor types. Using this database it is shown that the proposed method is not only robust, accurate and sensor invariant but also extremely efficient, thus making it suitable for real-life biometric applications.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129513887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425359
A. Amar, A. Weiss
Fundamental limitations on estimation accuracy are well known and include a variety of lower bounds including the celebrated Cramer Rao Lower Bound. However, similar theoretical limitations on resolution have not yet been presented. We exploit results from detection theory for deriving fundamental limitations on resolution. In this paper we discuss the resolution of two zero mean complex random Gaussian signals with a general and predefined covariance matrix observed with additive white Gaussian noise. The results are not based on any specific resolution technique and thus hold for any method and any resolution success rate. The theoretical limit is a simple expression of the observation interval, the user's pre-specified resolution success rate and the second derivative of the covariance matrix. We apply the results to the bearing resolution of two emitters with closely spaced direction of arrival impinging on an array of sensors. The derived limits are verified experimentally by model order selection methods such as the Akaike Information Criterion and the Minimum Description Length.
{"title":"Resolution limits of closely spaced random signals given the desired success rate","authors":"A. Amar, A. Weiss","doi":"10.1109/AVSS.2007.4425359","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425359","url":null,"abstract":"Fundamental limitations on estimation accuracy are well known and include a variety of lower bounds including the celebrated Cramer Rao Lower Bound. However, similar theoretical limitations on resolution have not yet been presented. We exploit results from detection theory for deriving fundamental limitations on resolution. In this paper we discuss the resolution of two zero mean complex random Gaussian signals with a general and predefined covariance matrix observed with additive white Gaussian noise. The results are not based on any specific resolution technique and thus hold for any method and any resolution success rate. The theoretical limit is a simple expression of the observation interval, the user's pre-specified resolution success rate and the second derivative of the covariance matrix. We apply the results to the bearing resolution of two emitters with closely spaced direction of arrival impinging on an array of sensors. The derived limits are verified experimentally by model order selection methods such as the Akaike Information Criterion and the Minimum Description Length.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129580439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425314
Tatsuya Osawa, Xiaojun Wu, K. Sudo, K. Wakabayashi, Hiroyuki Arai, T. Yasuno
In this paper, we present a new approach for the stable tracking of variable interacting targets under severe occlusion in 3D space. We formulate the state of multiple targets as a union state space of each target, and recursively estimate the multi-body configuration and the position of each target in 3D space by using the framework of Trans-dimensional Markov Chain Monte Carlo(MCMC). The 3D environmental model, which replicates the real-world 3D structure, is used for handling occlusions created by fixed objects in the environment, and reliably estimating the number of targets in the monitoring area. Experiments show that our system can stably track multiple humans that are interacting with each other and entering and leaving the monitored area.
{"title":"MCMC based multi-body tracking using full 3D model of both target and environment","authors":"Tatsuya Osawa, Xiaojun Wu, K. Sudo, K. Wakabayashi, Hiroyuki Arai, T. Yasuno","doi":"10.1109/AVSS.2007.4425314","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425314","url":null,"abstract":"In this paper, we present a new approach for the stable tracking of variable interacting targets under severe occlusion in 3D space. We formulate the state of multiple targets as a union state space of each target, and recursively estimate the multi-body configuration and the position of each target in 3D space by using the framework of Trans-dimensional Markov Chain Monte Carlo(MCMC). The 3D environmental model, which replicates the real-world 3D structure, is used for handling occlusions created by fixed objects in the environment, and reliably estimating the number of targets in the monitoring area. Experiments show that our system can stably track multiple humans that are interacting with each other and entering and leaving the monitored area.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126799947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425304
Evan Tan, Jing Chen
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a Region Of Interest (ROI) of a road in a traffic video. Firstly, low-level features are extracted from the ROI of each frame. Secondly, an unsupervised clustering algorithm called AutoClass is then applied to the low-level features to obtain a set of clusters for each pre-defined traffic density state. Finally, four HMM models are constructed for each traffic state respectively with each cluster corresponding to a state in the HMM and the structure of HMM is determined based on the cluster information. This approach improves over previous approaches that used Gaussian Mixture HMMs (GMHMM) by circumventing the need to make an arbitrary choice on the structure of the HMM as well as determining the number of mixtures used for each density traffic state. The results show that this approach can classify the traffic density in a ROI of a traffic video accurately with the property of being able to handle the varying illumination elegantly.
{"title":"Vehicular traffic density estimation via statistical methods with automated state learning","authors":"Evan Tan, Jing Chen","doi":"10.1109/AVSS.2007.4425304","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425304","url":null,"abstract":"This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a Region Of Interest (ROI) of a road in a traffic video. Firstly, low-level features are extracted from the ROI of each frame. Secondly, an unsupervised clustering algorithm called AutoClass is then applied to the low-level features to obtain a set of clusters for each pre-defined traffic density state. Finally, four HMM models are constructed for each traffic state respectively with each cluster corresponding to a state in the HMM and the structure of HMM is determined based on the cluster information. This approach improves over previous approaches that used Gaussian Mixture HMMs (GMHMM) by circumventing the need to make an arbitrary choice on the structure of the HMM as well as determining the number of mixtures used for each density traffic state. The results show that this approach can classify the traffic density in a ROI of a traffic video accurately with the property of being able to handle the varying illumination elegantly.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127936004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425318
S. Guler, Jason A. Silverstein, Ian A. Pushee
This paper presents an approach to detect stationary foreground objects in naturally busy surveillance video scenes with several moving objects. Our approach is inspired by human's visual cognition processes and builds upon a multi-tier video tracking paradigm with main layers being the spatially based "peripheral tracking" loosely corresponding to the peripheral vision and the object based "vision tunnels " for focused attention and analysis of tracked objects. Humans allocate their attention to different aspects of objects and scenes based on a defined task. In our model, a specific processing layer corresponding to allocation of attention is used for detection of objects that become stationary. The static object layer, a natural extension of this framework, detects and maintains the stationary foreground objects by using the moving object and scene information from Peripheral Tracker and the Scene Description layers. Simple event detection modules then use the enduring stationary objects to determine events such as Parked Vehicles or Abandoned Bags.
{"title":"Stationary objects in multiple object tracking","authors":"S. Guler, Jason A. Silverstein, Ian A. Pushee","doi":"10.1109/AVSS.2007.4425318","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425318","url":null,"abstract":"This paper presents an approach to detect stationary foreground objects in naturally busy surveillance video scenes with several moving objects. Our approach is inspired by human's visual cognition processes and builds upon a multi-tier video tracking paradigm with main layers being the spatially based \"peripheral tracking\" loosely corresponding to the peripheral vision and the object based \"vision tunnels \" for focused attention and analysis of tracked objects. Humans allocate their attention to different aspects of objects and scenes based on a defined task. In our model, a specific processing layer corresponding to allocation of attention is used for detection of objects that become stationary. The static object layer, a natural extension of this framework, detects and maintains the stationary foreground objects by using the moving object and scene information from Peripheral Tracker and the Scene Description layers. Simple event detection modules then use the enduring stationary objects to determine events such as Parked Vehicles or Abandoned Bags.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114553868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425324
I. Bosch, S. Gomez, L. Vergara, J. Moragues
This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.
{"title":"Infrared image processing and its application to forest fire surveillance","authors":"I. Bosch, S. Gomez, L. Vergara, J. Moragues","doi":"10.1109/AVSS.2007.4425324","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425324","url":null,"abstract":"This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116271669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425286
P. Tu, N. Krahnstoever, J. Rittscher
Using a detect and track paradigm, we present a surveillance framework where each camera uses local resources to perform real-time person detection. These detections are then processed by a distributed site-wide tracking system. The detectors themselves are based on boosted user-defined exemplars, which capture both appearance and shape information. The detectors take integral images of both intensity and Sobel responses as input. This data representation enables efficient processing without relying on background subtraction or other motion cues. View-specific person detectors are constructed by iteratively presenting the boosting algorithm with training data associated with each individual camera. These detections are then transmitted from a distributed set of tracking clients to a server, which maintains a set of site-wide target tracks. Automatic calibration methods allow for tracking to be performed in a ground plane representation, which enables effective camera hand-off. Factors such as network latencies and scalability will be discussed.
{"title":"View adaptive detection and distributed site wide tracking","authors":"P. Tu, N. Krahnstoever, J. Rittscher","doi":"10.1109/AVSS.2007.4425286","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425286","url":null,"abstract":"Using a detect and track paradigm, we present a surveillance framework where each camera uses local resources to perform real-time person detection. These detections are then processed by a distributed site-wide tracking system. The detectors themselves are based on boosted user-defined exemplars, which capture both appearance and shape information. The detectors take integral images of both intensity and Sobel responses as input. This data representation enables efficient processing without relying on background subtraction or other motion cues. View-specific person detectors are constructed by iteratively presenting the boosting algorithm with training data associated with each individual camera. These detections are then transmitted from a distributed set of tracking clients to a server, which maintains a set of site-wide target tracks. Automatic calibration methods allow for tracking to be performed in a ground plane representation, which enables effective camera hand-off. Factors such as network latencies and scalability will be discussed.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125956445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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