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2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance最新文献

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PETS2010: Dataset and Challenge PETS2010:数据集和挑战
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.90
J. Ferryman, A. Ellis
{"title":"PETS2010: Dataset and Challenge","authors":"J. Ferryman, A. Ellis","doi":"10.1109/AVSS.2010.90","DOIUrl":"https://doi.org/10.1109/AVSS.2010.90","url":null,"abstract":"This paper describes the crowd image analysis challenge that forms part of the PETS 2010 workshop. The aim of this challenge is to use new or existing systems for i) crowd count and density estimation, ii) tracking of individual(s) within a crowd, and iii) detection of separate flows and specific crowd events, in a real-world environment. The dataset scenarios were filmed from multiple cameras and involve multiple actors.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129361399","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: 98
Trajectory Based Activity Discovery 基于轨迹的活动发现
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.15
Guido Pusiol, F. Brémond, M. Thonnat
{"title":"Trajectory Based Activity Discovery","authors":"Guido Pusiol, F. Brémond, M. Thonnat","doi":"10.1109/AVSS.2010.15","DOIUrl":"https://doi.org/10.1109/AVSS.2010.15","url":null,"abstract":"This paper proposes a framework to discover activities inan unsupervised manner, and add semantics with minimalsupervision. The framework uses basic trajectory informationas input and goes up to video interpretation. The workreduces the gap between low-level information and semanticinterpretation, building an intermediate layer composedof Primitive Events. The proposed representation for primitiveevents aims at capturing small meaningful motions overthe scene with the advantage of being learnt in an unsupervisedmanner. We propose the discovery of an activity usingthese Primitive Events as the main descriptors. The activitydiscovery is done using only real tracking data. Semanticsare added to the discovered activities and the recognition ofactivities (e.g., “Cooking”, “Eating”) can be automaticallydone with new datasets. Finally we validate the descriptorsby discovering and recognizing activities in a home careapplication dataset.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"72 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016822","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: 10
A Local Directional Pattern Variance (LDPv) Based Face Descriptor for Human Facial Expression Recognition 基于局部方向模式方差(LDPv)的人脸描述子用于人脸表情识别
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.9
M. H. Kabir, T. Jabid, O. Chae
{"title":"A Local Directional Pattern Variance (LDPv) Based Face Descriptor for Human Facial Expression Recognition","authors":"M. H. Kabir, T. Jabid, O. Chae","doi":"10.1109/AVSS.2010.9","DOIUrl":"https://doi.org/10.1109/AVSS.2010.9","url":null,"abstract":"Automatic facial expression recognition is a challengingproblem in computer vision, and has gained significantimportance in applications of human-computer interaction.This paper presents a new appearance-based feature descriptor,the Local Directional Pattern Variance (LDPv), torepresent facial components for human expression recognition.In contrast with LDP, the proposed LDPv introducesthe local variance of directional responses to encodethe contrast information within the descriptor. Here,the LDPv represenation characterizes both spatial structureand contrast information of each micro-patterns. Templatematching and Support Vector Machine (SVM) classifierare used to classify the LDPv feature vector of differentprototypic expression images. Experimental results usingthe Cohn-Kanade database show that the LDPv descriptoryields an improved recognition rate, as compared to existingappearance-based feature descriptors, such as the Gaborwaveletand Local Binary Pattern (LBP).","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121020644","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: 77
Performance Evaluation of a People Tracking System on PETS2009 Database 基于PETS2009数据库的人员跟踪系统性能评价
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.87
Donatello Conte, P. Foggia, G. Percannella, M. Vento
{"title":"Performance Evaluation of a People Tracking System on PETS2009 Database","authors":"Donatello Conte, P. Foggia, G. Percannella, M. Vento","doi":"10.1109/AVSS.2010.87","DOIUrl":"https://doi.org/10.1109/AVSS.2010.87","url":null,"abstract":"In this paper a system for autonomous video surveillance in relatively unconstrained environments is described. The system consists of two principal phases: object detection and object tracking. An adaptive background subtraction, together with a set of corrective algorithms, is used to cope with variable lighting, dynamic and articulate scenes, etc. The tracking algorithm is based on a matrix representation of the problem, and is used to face splitting and occlusion problems. When the tracking algorithm fails in following actual object trajectories, an appearancebased module is used to restore object identities. An experimental evaluation, carried out on the PETS2009 dataset for tracking, shows promising results.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115545462","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: 40
Learning of Scene-Specific Object Detectors by Classifier Co-Grids 基于分类器协同网格的场景特定目标检测器学习
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.10
Sabine Sternig, P. Roth, H. Bischof
{"title":"Learning of Scene-Specific Object Detectors by Classifier Co-Grids","authors":"Sabine Sternig, P. Roth, H. Bischof","doi":"10.1109/AVSS.2010.10","DOIUrl":"https://doi.org/10.1109/AVSS.2010.10","url":null,"abstract":"Recently, classifier grids have shown to be a considerablealternative to sliding window approaches for objectdetection from static cameras. The main drawback of suchmethods is that they are biased by the initial model. In fact,the classifiers can be adapted to changing environmentalconditions but due to conservative updates no new objectspecificinformation is acquired. Thus, the goal of this workis to increase the recall of scene-specific classifiers whilepreserving their accuracy and speed. In particular, we introducea co-training strategy for classifier grids using arobust on-line learner. Thus, the robustness is preservedwhile the recall can be increased. The co-training strategyrobustly provides negative as well as positive updates. Inaddition, the number of negative updates can be drasticallyreduced, which additionally speeds up the system. In theexperimental results these benefits are demonstrated on differentpublicly available surveillance benchmark data sets.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694572","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: 7
A Framework for an Event Driven Video Surveillance System 事件驱动视频监控系统的框架
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.57
Declan F. Kieran, Weiqi Yan
{"title":"A Framework for an Event Driven Video Surveillance System","authors":"Declan F. Kieran, Weiqi Yan","doi":"10.1109/AVSS.2010.57","DOIUrl":"https://doi.org/10.1109/AVSS.2010.57","url":null,"abstract":"In this paper we present an event driven surveillance system.The purpose of this system is to enable thorough explorationof surveillance events. The system uses a clientserverweb architecture as this provides scalability for furtherdevelopment of the system infrastructure. The systemis designed to be accessed by surveillance operators whocan review and comment on events generated by our eventdetection processing modules. The presentation interfaceis based around a cross between Gmail and YouTube, aswe believe these interfaces to be intuitive for ordinary computeroperators. Our motivation is to fully utilize the eventsarchived in our database and to further refine the relevantevents. We do not just focus on event detection, but areworking towards the optimization of event detection. To thebest of our knowledge this system provides a novel approachto the technological surveillance paradigm.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195276","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: 33
Group Level Activity Recognition in Crowded Environments across Multiple Cameras 拥挤环境中跨多个摄像头的群体级活动识别
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.65
Ming-Ching Chang, N. Krahnstoever, Ser-Nam Lim, Ting Yu
{"title":"Group Level Activity Recognition in Crowded Environments across Multiple Cameras","authors":"Ming-Ching Chang, N. Krahnstoever, Ser-Nam Lim, Ting Yu","doi":"10.1109/AVSS.2010.65","DOIUrl":"https://doi.org/10.1109/AVSS.2010.65","url":null,"abstract":"Environments such as schools, public parks and prisonsand others that contain a large number of people are typi-cally characterized by frequent and complex social interac-tions. In order to identify activities and behaviors in suchenvironments, it is necessary to understand the interactionsthat take place at a group level. To this end, this paper ad-dresses the problem of detecting and predicting suspiciousand in particular aggressive behaviors between groups ofindividuals such as gangs in prison yards. The work buildson a mature multi-camera multi-target person tracking sys-tem that operates in real-time and has the ability to han-dle crowded conditions. We consider two approaches forgrouping individuals: (i) agglomerative clustering favoredby the computer vision community, as well as (ii) decisiveclustering based on the concept of modularity, which is fa-vored by the social network analysis community. We showthe utility of such grouping analysis towards the detectionof group activities of interest. The presented algorithm isintegrated with a system operating in real-time to success-fully detect highly realistic aggressive behaviors enacted bycorrectional officers in a simulated prison environment. Wepresent results from these enactments that demonstrate theefficacy of our approach.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198827","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: 64
Background Subtraction under Sudden Illumination Changes 光照突然变化下的背景减法
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.72
L. Vosters, Caifeng Shan, T. Gritti
{"title":"Background Subtraction under Sudden Illumination Changes","authors":"L. Vosters, Caifeng Shan, T. Gritti","doi":"10.1109/AVSS.2010.72","DOIUrl":"https://doi.org/10.1109/AVSS.2010.72","url":null,"abstract":"Robust background subtraction under sudden illuminationchanges is a challenging problem. In this paper, wepropose an approach to address this issue, which combinesthe Eigenbackground algorithm together with a statisticalillumination model. The rst algorithm is used to give arough reconstruction of the input frame, while the secondone improves the foreground segmentation. We introduce anonline spatial likelihood model by detecting reliable backgroundand foreground pixels. Experimental results illustratethat our approach achieves consistently higher accuracycompared to several state-of-the-art algorithms","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819558","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: 41
Thirteen Hard Cases in Visual Tracking
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.85
D. M. Chu, A. Smeulders
{"title":"Thirteen Hard Cases in Visual Tracking","authors":"D. M. Chu, A. Smeulders","doi":"10.1109/AVSS.2010.85","DOIUrl":"https://doi.org/10.1109/AVSS.2010.85","url":null,"abstract":"Visual tracking is a fundamental task in computer vision. However there has been no systematic way of analyzing visual trackers so far. In this paper we propose a method that can help researchers determine strengths and weaknesses of any visual tracker. To this end, we consider visual tracking as an isolated problem and decompose it into fundamental and independent subproblems. Each subproblem is designed to associate with a different tracking circumstance. By evaluating a visual tracker onto a specific subproblem, we can determine how good it is with respect to that dimension. In total we come up with thirteen subproblems in our decomposition. We demonstrate the use of our proposed method by analyzing working conditions of two state-of-theart trackers.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"32 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125709716","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: 31
Counting People in Crowded Environments by Fusion of Shape and Motion Information 基于形状和运动信息融合的拥挤环境人口统计
2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.92
Michael Pätzold, Rubén Heras Evangelio, T. Sikora
{"title":"Counting People in Crowded Environments by Fusion of Shape and Motion Information","authors":"Michael Pätzold, Rubén Heras Evangelio, T. Sikora","doi":"10.1109/AVSS.2010.92","DOIUrl":"https://doi.org/10.1109/AVSS.2010.92","url":null,"abstract":"Knowing the number of people in a crowded scene is of big interest in the surveillance scene. In the past, this problem has been tackled mostly in an indirect, statistical way. This paper presents a direct, counting by detection, method based on fusing spatial information received from an adapted Histogram of Oriented Gradientsalgorithm (HOG) with temporal information by exploiting distinctive motion characteristics of different human body parts. For that purpose, this paper defines a measure for uniformity of motion. Furthermore, the system performance is enhanced by validating the resulting human hypotheses by tracking and applying a coherent motion detection. The approach is illustrated with an experimental evaluation.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116447693","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: 49
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