2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance最新文献

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Contextual Constraints for Person Retrieval in Camera Networks 摄像机网络中人检索的语境约束
M. Bäuml, Makarand Tapaswi, Arne Schumann, R. Stiefelhagen
{"title":"Contextual Constraints for Person Retrieval in Camera Networks","authors":"M. Bäuml, Makarand Tapaswi, Arne Schumann, R. Stiefelhagen","doi":"10.1109/AVSS.2012.28","DOIUrl":"https://doi.org/10.1109/AVSS.2012.28","url":null,"abstract":"We use contextual constraints for person retrieval in camera networks. We start by formulating a set of general positive and negative constraints on the identities of person tracks in camera networks, such as a person cannot appear twice in the same frame. We then show how these constraints can be used to improve person retrieval. First, we use the constraints to obtain training data in an unsupervised way to learn a general metric that is better suited to discriminate between different people than the Euclidean distance. Second, starting from an initial query track, we enhance the query-set using the constraints to obtain additional positive and negative samples for the query. Third, we formulate the person retrieval task as an energy minimization problem, integrate track scores and constraints in a common framework and jointly optimize the retrieval over all interconnected tracks. We evaluate our approach on the CAVIAR dataset and achieve 22% relative performance improvement in terms of mean average precision over standard retrieval where each track is treated independently.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125976203","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}
引用次数: 2
Water Filling: Unsupervised People Counting via Vertical Kinect Sensor 注水:通过垂直Kinect传感器进行无监督计数
Xucong Zhang, Junjie Yan, Shikun Feng, Zhen Lei, Dong Yi, S. Li
{"title":"Water Filling: Unsupervised People Counting via Vertical Kinect Sensor","authors":"Xucong Zhang, Junjie Yan, Shikun Feng, Zhen Lei, Dong Yi, S. Li","doi":"10.1109/AVSS.2012.82","DOIUrl":"https://doi.org/10.1109/AVSS.2012.82","url":null,"abstract":"People counting is one of the key components in video surveillance applications, however, due to occlusion, illumination, color and texture variation, the problem is far from being solved. Different from traditional visible camera based systems, we construct a novel system that uses vertical Kinect sensor for people counting, where the depth information is used to remove the affect of the appearance variation. Since the head is always closer to the Kinect sensor than other parts of the body, people counting task equals to find the suitable local minimum regions. According to the particularity of the depth map, we propose a novel unsupervised water filling method that can find these regions with the property of robustness, locality and scale-invariance. Experimental comparisons with mean shift and random forest on two databases validate the superiority of our water filling algorithm in people counting.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429198","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}
引用次数: 110
People Count Estimation In Small Crowds 人们在小群体中计算估算
Pietro Morerio, L. Marcenaro, C. Regazzoni
{"title":"People Count Estimation In Small Crowds","authors":"Pietro Morerio, L. Marcenaro, C. Regazzoni","doi":"10.1109/AVSS.2012.88","DOIUrl":"https://doi.org/10.1109/AVSS.2012.88","url":null,"abstract":"This work addresses the problem of people counting in crowded situations, such as urban environments, in computer vision. As crowding density increases in a scene, it might become impossible to count people as single individuals: a global group-based approach is then preferable and in fact often necessary. A simple method for estimating the count of people in such tight crowds is here proposed, relying on accurate camera calibration. A training phase is also needed by the algorithm in order to learn the parameters needed for estimation.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127190944","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}
引用次数: 26
Fast Crowd Density Estimation in Surveillance Videos without Training 未经训练的监控视频快速人群密度估计
Zhong Zhang, Weihong Yin, P. L. Venetianer
{"title":"Fast Crowd Density Estimation in Surveillance Videos without Training","authors":"Zhong Zhang, Weihong Yin, P. L. Venetianer","doi":"10.1109/AVSS.2012.38","DOIUrl":"https://doi.org/10.1109/AVSS.2012.38","url":null,"abstract":"Crowd analytics is becoming a highly desirable feature of Intelligent Video Surveillance (IVS) applications. In this paper we propose a new, practical approach that adds very little computational and configuration overhead to an IVS system. The approach extends a standard IVS system, using available video content analysis data and camera calibration information to provide accurate human count estimation in crowded scenarios. The algorithm is viewpoint independent and requires no training for different camera views. The primary output of the algorithm is a real-time crowd density measurement at each image location. This can be further used to detect various crowd related events. Extensive experiments show that the approach is robust and it has been integrated into a commercially available IVS system.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661159","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}
引用次数: 6
People Counting across Multiple Cameras for Intelligent Video Surveillance 智能视频监控的多摄像头人数统计
Jingwen Li, Lei Huang, Chang-ping Liu
{"title":"People Counting across Multiple Cameras for Intelligent Video Surveillance","authors":"Jingwen Li, Lei Huang, Chang-ping Liu","doi":"10.1109/AVSS.2012.54","DOIUrl":"https://doi.org/10.1109/AVSS.2012.54","url":null,"abstract":"Pedestrian counting is widely used in civilian surveillance. In this paper, we present a people counting system which estimates the number of people across multiple cameras with partial overlapping Fields Of Views (FOVs). The main contributions of this paper include: 1) we propose a multi-object detection and tracking method by means of synthesizing the local-feature-level information into object-level based on an electing and weighting mechanism (EWM), 2) We present a scheme to integrate the counting results from multiple cameras. Through homograpy transform and similarity measurement rules, the system can find the objects in overlapping FOVs and finally estimate the integrated number of people across multiple cameras. Experiments results demonstrate that our system is effective and accurate for multi-camera people counting.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127855432","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}
引用次数: 18
Towards Gesture-Based User Authentication 迈向基于手势的用户认证
Kam Lai, J. Konrad, P. Ishwar
{"title":"Towards Gesture-Based User Authentication","authors":"Kam Lai, J. Konrad, P. Ishwar","doi":"10.1109/AVSS.2012.77","DOIUrl":"https://doi.org/10.1109/AVSS.2012.77","url":null,"abstract":"Video cameras are extensively used in modern surveillance systems to detect, track, and recognize, objects, people, and anomalies. Their use in user authentication, however, has been limited primarily to close-range face recognition systems. In this paper, we explore user authentication based on gestures captured by a video camera. Unlike pure biometrics, such as fingerprints, iris scans, and faces, gesture-based authentication combines irrevocable biometric information, such as the shapes and relative sizes of body parts, with voluntary movements which can be revoked. Our authentication method applies the empirical feature covariance matrix framework that has previously been used for tracking, face localization, and action recognition, to features extracted from body silhouettes. We have tested the performance of our algorithm in both user classification and user authentication on a database of 20 individuals performing 8 different gestures. We have obtained a 93-99% Correct Classification Rate (CCR) for user classification and a 5-6% Equal Error Rate (EER) for user authentication on single gestures from this dataset. This is a very encouraging result suggesting that gesture-based user authentication may be feasible in scenarios with a limited number of users.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126629125","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}
引用次数: 34
Angular Heuristics for Coverage Maximization in Multi-camera Surveillance 多摄像机监控中覆盖最大化的角度启发式算法
Ahmed Abdelkader Abdelrazek, M. Mokhtar, H. El-Alfy
{"title":"Angular Heuristics for Coverage Maximization in Multi-camera Surveillance","authors":"Ahmed Abdelkader Abdelrazek, M. Mokhtar, H. El-Alfy","doi":"10.1109/AVSS.2012.11","DOIUrl":"https://doi.org/10.1109/AVSS.2012.11","url":null,"abstract":"Multiple cameras are used to track targets moving amongst obstacles. Surveillance video streamed from a top-view camera is processed to control the orientation of multiple pan-tilt-zoom cameras to cover as many targets as possible at high resolutions. The problem of maximizing the number of covered targets with a set of cameras has been shown to be computationally expensive and hence, several approximations have been suggested in the literature. We develop our own ones, compare them to some existing approaches by extensive simulation and show their superiority. Our new heuristics make an attempt at continuous panning that is needed when moving to real world experimentation to achieve seamless target tracking.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125731608","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}
引用次数: 5
A Robust Algorithm for the Detection of Vehicle Turn Signals and Brake Lights 一种鲁棒的车辆转向信号和刹车灯检测算法
Mauricio Casares, A. Almagambetov, Senem Velipasalar
{"title":"A Robust Algorithm for the Detection of Vehicle Turn Signals and Brake Lights","authors":"Mauricio Casares, A. Almagambetov, Senem Velipasalar","doi":"10.1109/AVSS.2012.2","DOIUrl":"https://doi.org/10.1109/AVSS.2012.2","url":null,"abstract":"Robust and lightweight detection of alert signals of front vehicle, such as turn signals and brake lights, is extremely critical, especially in autonomous vehicle applications. Even with cars that are driven by human beings, automatic detection of these signals can aid in the prevention of otherwise deadly accidents. This paper presents a novel, robust and lightweight algorithm for detecting brake lights and turn signals both at night and during the day. The proposed method employs a Kalman filter to reduce the processing load. Much research is focused only on the detection of brake lights at night, but our algorithm is able to detect turn signals as well as brake lights under any lighting conditions with high accuracy rates.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115983144","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}
引用次数: 38
Robust Orientation and Appearance Adaptation for Wide-Area Large Format Video Object Tracking 广域大画幅视频目标跟踪的鲁棒方向和外观自适应
R. Pelapur, K. Palaniappan, G. Seetharaman
{"title":"Robust Orientation and Appearance Adaptation for Wide-Area Large Format Video Object Tracking","authors":"R. Pelapur, K. Palaniappan, G. Seetharaman","doi":"10.1109/AVSS.2012.92","DOIUrl":"https://doi.org/10.1109/AVSS.2012.92","url":null,"abstract":"Visual feature-based tracking systems need to adapt to variations in the appearance of an object and in the scene for robust performance. Though these variations may be small for short time steps, they can accumulate over time and deteriorate the quality of the matching process across longer intervals. Tracking in aerial imagery can be challenging as viewing geometry, calibration inaccuracies, complex ight paths and background changes combined with illumination changes, and occlusions can result in rapid appearance change of objects. Balancing appearance adaptation with stability to avoid tracking non-target objects can lead to longer tracks which is an indicator of tracker robustness. The approach described in this paper can handle affine changes such as rotation by explicit orientation estimation, scale changes by using a multiscale Hessian edge detector and drift correction by using segmentation. We propose an appearance update approach that handles the 'drifting' problem using this adaptive scheme within a tracking environment that is comprised of a rich feature set and a motion model.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127713549","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}
引用次数: 22
Qualitative Evaluation of Detection and Tracking Performance 检测和跟踪性能的定性评价
S. Sankaranarayanan, F. Brémond, D. Tax
{"title":"Qualitative Evaluation of Detection and Tracking Performance","authors":"S. Sankaranarayanan, F. Brémond, D. Tax","doi":"10.1109/AVSS.2012.57","DOIUrl":"https://doi.org/10.1109/AVSS.2012.57","url":null,"abstract":"A new evaluation approach for detection and tracking systems is presented in this work. Given an algorithm that detects people and simultaneously tracks them, we evaluate its output by considering the complexity of the input scene. Some videos used for the evaluation are recorded using the Kinect sensor which provides for an automated ground truth acquisition system. To analyze the algorithm performance, a number of reasons due to which an algorithm might fail is investigated and quantified over the entire video sequence. A set of features called Scene Complexity measures are obtained for each input frame. The variability in the algorithm performance is modeled by these complexity measures using a polynomial regression model. From the regression statistics, we show that we can compare the performance of two different algorithms and also quantify the relative influence of the scene complexity measures on a given algorithm.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123214434","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}
引用次数: 1
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