{"title":"Activity Topology Estimation for Large Networks of Cameras","authors":"A. Hengel, A. Dick, R. Hill","doi":"10.1109/AVSS.2006.17","DOIUrl":"https://doi.org/10.1109/AVSS.2006.17","url":null,"abstract":"Estimating the paths that moving objects can take through the fields of view of possibly non-overlapping cameras, also known as their activity topology, is an important step in the effective interpretation of surveillance video. Existing approaches to this problem involve tracking moving objects within cameras, and then attempting to link tracks across views. In contrast we propose an approach which begins by assuming all camera views are potentially linked, and successively eliminates camera topologies that are contradicted by observed motion. Over time, the true patterns of motion emerge as those which are not contradicted by the evidence. These patterns may then be used to initialise a finer level search using other approaches if required. This method thus represents an efficient and effective way to learn activity topology for a large network of cameras, particularly with a limited amount of data.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128391244","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}
{"title":"Analyzing Human Movements from Silhouettes Using Manifold Learning","authors":"Liang Wang, D. Suter","doi":"10.1109/AVSS.2006.25","DOIUrl":"https://doi.org/10.1109/AVSS.2006.25","url":null,"abstract":"A novel method for learning and recognizing sequential image data is proposed, and promising applications to vision-based human movement analysis are demonstrated. To find more compact representations of high-dimensional silhouette data, we exploit locality preserving projections (LPP) to achieve low-dimensional manifold embedding. Further, we present two kinds of methods to analyze and recognize learned motion manifolds. One is correlation matching based on the Hausdorrf distance, and the other is a probabilistic method using continuous hidden Markov models (HMM). Encouraging results are obtained in two representative experiments in the areas of human activity recognition and gait-based human identification.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114196071","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}
{"title":"On the Performance and Use of Speaker Recognition Systems for Surveillance","authors":"P. Barger, S. Sridharan","doi":"10.1109/AVSS.2006.87","DOIUrl":"https://doi.org/10.1109/AVSS.2006.87","url":null,"abstract":"We model the performance of a speaker recognition system used for surveillance to prioritize a large number of candidate speakers in search of a single target speaker. It is assumed that the system operates by ordering all speakers in order from best match to worst match, with the goal of having the true speaker sample positioned as high as possible on the list. Some performance measures for prioritization systems are given and are applied to a real speaker recognition system. An analytic expression for the probability density function of the true speaker's position on the list is found, subject to basic assumptions concerning the distribution of true speaker and false speaker scores. A comparison is made to the performance of a system which is operating only by making verification decisions, and it is shown that making soft decisions results in significantly better surveillance performance.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128266554","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}
{"title":"Nonparametric Background Modeling Using the CONDENSATION Algorithm","authors":"Xingzhi Luo, S. Bhandarkar, Wei Hua, Haisong Gu","doi":"10.1109/AVSS.2006.81","DOIUrl":"https://doi.org/10.1109/AVSS.2006.81","url":null,"abstract":"Background modeling for dynamic scenes is an important problem in the context of real time video surveillance systems. Several nonparametric background models have been proposed to model dynamic scenes and promising results have been reported. However, a critical problem with existing nonparametric models is their high computational requirement because a large set of background samples is usually needed to model the background. In this paper, a nonparametric background model that uses an importance sampling method is proposed to overcome the problem of high computational complexity of conventional nonparametric background models. Instead of using a large number of samples to model the background probability densities, much fewer background samples are maintained and updated using the CONDENSATION algorithm. A Markov Random Field model is used to enhance the foreground detection results by imposing spatial constraints. Experimental results show that the proposed method is much faster and computationally more efficient than existing nonparametric background models. The proposed technique is observed to match the capabilities of existing nonparametric background models in terms of being able to effectively model dynamic backgrounds but with greatly reduced computational complexity.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133238633","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}
Henry Detmold, A. Dick, K. Falkner, David S. Munro, A. Hengel, R. Morrison
{"title":"Scalable Surveillance Software Architecture","authors":"Henry Detmold, A. Dick, K. Falkner, David S. Munro, A. Hengel, R. Morrison","doi":"10.1109/AVSS.2006.101","DOIUrl":"https://doi.org/10.1109/AVSS.2006.101","url":null,"abstract":"Video surveillance is a key technology for enhanced protection of facilities such as airports and power stations from various types of threat. Networks of thousands of IP-based cameras are now possible, but current surveillance methodologies become increasingly ineffective as the number of cameras grows. Constructing software that efficiently and reliably deals with networks of this size is a distributed information processing problem as much as it is a video interpretation challenge. This paper demonstrates a software architecture approach to the construction of large scale surveillance network software and explores the implications for instantiating surveillance algorithms at such a scale. A novel architecture for video surveillance is presented, and its efficacy demonstrated through application to an important class of surveillance algorithms.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116577221","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}
{"title":"Human Body Part Labeling and Tracking Using Graph Matching Theory","authors":"Nicolas Thome, D. Merad, S. Miguet","doi":"10.1109/AVSS.2006.59","DOIUrl":"https://doi.org/10.1109/AVSS.2006.59","url":null,"abstract":"Properly labeling human body parts in video sequences is essential for robust tracking and motion interpretation frameworks. We propose to perform this task by using Graph Matching. The silhouette skeleton is computed and decomposed into a set of segments corresponding to the different limbs. A Graph capturing the topology of the segments is generated and matched against a 3D model of the human skeleton. The limb identification is carried out for each node of the graph, potentially leading to the absence of correspondence. The method captures the minimal information about the skeleton shape. No assumption about the viewpoint, the human pose, the geometry or the appearance of the limbs is done during the matching process, making the approach applicable to every configuration. Some correspondences that might be ambiguous only relying on topology are enforced by tracking each graph node over time. Several results present the efficiency of the labeling, particularly its robustness to limb detection errors that are likely to occur in real situations because of occlusions or low level system failures. Finally the relevance of the labeling in an overall tracking system is described.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614680","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}
{"title":"Skin Colour-Based Face Detection in Colour Images","authors":"W. Lai, Chang-Tsun Li","doi":"10.1109/AVSS.2006.105","DOIUrl":"https://doi.org/10.1109/AVSS.2006.105","url":null,"abstract":"We propose in this work a method for detecting faces in colour images with complex backgrounds. The approach starts with the transformation of the image pixels from the RGB colour space to the chrominance space (YCbCr). Secondly, a Gaussian model is fitted on the transformed image in order to calculate the likelihood of skin for each pixel and to create a likelihood image. Thirdly, by thresholding the likelihood image, skin pixels are segmented to form a binary skin map, which contains the candidate face regions. Finally, a verification process is carried out to determine whether these candidate face regions are real faces or not.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122246352","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}
{"title":"An Active Head Tracking System for Distance Education and Videoconferencing Applications","authors":"S. Huttunen, J. Heikkilä","doi":"10.1109/AVSS.2006.19","DOIUrl":"https://doi.org/10.1109/AVSS.2006.19","url":null,"abstract":"We present a system for automatic head tracking with a single pan-tilt-zoom (PTZ) camera. In distance education the PTZ tracking system developed can be used to follow a teacher actively when s/he moves in the classroom. In other videoconferencing applications the system can be utilized to provide a close-up view of the person all the time. Since the color features used in tracking are selected and updated online, the system can adapt to changes rapidly. The information received from the tracking module is used to actively control the PTZ camera in order to keep the person in the camera view. In addition, the system implemented is able to recover from erroneous situations. Preliminary experiments indicate that the PTZ system can perform well under different lighting conditions and large scale changes.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004502","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}
{"title":"VHDL-Based Simulation of a Parallel Implementation of a Phase-Based Algorithm for Optical Flow","authors":"Yasmine Nagy, M. Saeb, Y. El-Sonbaty","doi":"10.1109/AVSS.2006.114","DOIUrl":"https://doi.org/10.1109/AVSS.2006.114","url":null,"abstract":"The computation of optical flow can be an important part in a diverse number of applications. However, optical flow algorithms can be categorized as either very accurate and slow or very fast and highly inaccurate. None of the optical flow algorithms combined both accuracy and efficiency. Among these algorithms was the phase-based fleet and Jepson algorithm. Although this algorithm has proved to produce relatively accurate results, it can not be exploited in many real-life applications due to its relatively long run-time. The goal of this paper is to combine the accuracy of the phase-based optical flow algorithm by Fleet and Jepson and exploit the parallelism and high performance capabilities of the FPGAs to provide an accurate and efficient optical flow algorithm for FPGA-based applications.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278989","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}
{"title":"Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification","authors":"T. Gandhi, M. Trivedi","doi":"10.1109/AVSS.2006.90","DOIUrl":"https://doi.org/10.1109/AVSS.2006.90","url":null,"abstract":"This paper proposes a concept of panoramic appearance map to perform reidentification of a people who leave the scene and reappear after some time. The map is a compact signature of appearance information of a person extracted from multiple cameras. The person is detected and tracked in multiple cameras and triangulation is used to accurately localize the person in 3-D. A virtual cylinder is formed around the person's location and mapped onto an image with the horizontal axis representing the azimuth angle and vertical axis representing the height. Each bin in the map image gets the appearance information from all the cameras which can observe it. The maps between different tracks are matched using a weighted metric. Experimental results showing person matching and reidentification show the effectiveness of the approach.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130446179","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}