2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance最新文献_第2页

TrustCAM: Security and Privacy-Protection for an Embedded Smart Camera Based on Trusted Computing TrustCAM:基于可信计算的嵌入式智能摄像头的安全与隐私保护

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.38

Thomas Winkler, B. Rinner

引用次数: 70

A Method for Counting People in Crowded Scenes 一种在拥挤场景中计算人数的方法

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.78

Donatello Conte, P. Foggia, G. Percannella, Francesco Tufano, M. Vento

{"title":"A Method for Counting People in Crowded Scenes","authors":"Donatello Conte, P. Foggia, G. Percannella, Francesco Tufano, M. Vento","doi":"10.1109/AVSS.2010.78","DOIUrl":"https://doi.org/10.1109/AVSS.2010.78","url":null,"abstract":"This paper presents a novel method to count people forvideo surveillance applications. Methods in the literatureeither follow a direct approach, by first detecting people andthen counting them, or an indirect approach, by establishinga relation between some easily detectable scene featuresand the estimated number of people. The indirect approachis considerably more robust, but it is not easy to take intoaccount such factors as perspective or people groups withdifferent densities.The proposed technique, while based on the indirect approach,specifically addresses these problems; furthermoreit is based on a trainable estimator that does not requirean explicit formulation of a priori knowledge about the perspectiveand density effects present in the scene at hand.In the experimental evaluation, the method has beenextensively compared with the algorithm by Albiol et al.,which provided the highest performance at the PETS 2009contest on people counting. The experimentation has usedthe public PETS 2009 datasets. The results confirm that theproposed method improves the accuracy, while retaining therobustness of the indirect approach.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"139 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":"132431058","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: 87

Intelligent Sensor Information System For Public Transport – To Safely Go… 公共交通智能传感器信息系统-安全出行…

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.36

P. Miller, Weiru Liu, C. Fowler, Huiyu Zhou, Jiali Shen, Jianbing Ma, Jianguo Zhang, Weiqi Yan, K. Mclaughlin, S. Sezer

引用次数: 16

Local Abnormality Detection in Video Using Subspace Learning 基于子空间学习的视频局部异常检测

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.70

Ioannis Tziakos, A. Cavallaro, Li-Qun Xu

{"title":"Local Abnormality Detection in Video Using Subspace Learning","authors":"Ioannis Tziakos, A. Cavallaro, Li-Qun Xu","doi":"10.1109/AVSS.2010.70","DOIUrl":"https://doi.org/10.1109/AVSS.2010.70","url":null,"abstract":"On-line abnormality detection in video without the use ofobject detection and tracking is a desirable task in surveillance.We address this problem for the case when labeledinformation about normal events is limited and informationabout abnormal events is not available. We formulatethis problem as a one-class classification, where multiplelocal novelty classifiers (detectors) are used to first learnnormal actions based on motion information and then todetect abnormal instances. Each detector is associated toa small region of interest and is trained over labeled samplesprojected on an appropriate subspace. We discover thissubspace by using both labeled and unlabeled segments.We investigate the use of subspace learning and comparetwo methodologies based on linear (Principal ComponentsAnalysis) and on non-linear subspace learning (LocalityPreserving Projections), respectively. Experimental resultson a real underground station dataset shows that the linearapproach is better suited for cases where the subspacelearning is restricted to the labeled samples, whereas thenon-linear approach is preferable in the presence of additionalunlabeled data.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"83 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":"114139564","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: 23

Privacy-Aware Object Representation for Surveillance Systems 监控系统的隐私感知对象表示

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.73

Hauke Vagts, A. Bauer

引用次数: 11

Incremental Mosaicking of Images from Autonomous, Small-Scale UAVs 从自主，小型无人机图像的增量镶嵌

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.14

S. Yahyanejad, D. Wischounig-Strucl, M. Quaritsch, B. Rinner

引用次数: 60

Global Identification of Tracklets in Video Using Long Range Identity Sensors 基于远程身份传感器的视频轨迹全局识别

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.46

Xunyi Yu, A. Ganz

{"title":"Global Identification of Tracklets in Video Using Long Range Identity Sensors","authors":"Xunyi Yu, A. Ganz","doi":"10.1109/AVSS.2010.46","DOIUrl":"https://doi.org/10.1109/AVSS.2010.46","url":null,"abstract":"Reliable tracking of people in video and recovering theiridentities are of great importance to video analytics applications.For outdoor applications, long range identity sensorssuch as active RFID can provide good coverage in alarge open space, though they only provide coarse locationinformation. We propose a probabilistic approach usingnoisy inputs from multiple long range identity sensorsto globally associate and identify fragmented tracklets generatedby video tracking algorithms. We extend a networkflow based data association model to recover tracklet identityefficiently. Our approach is evaluated using five minutesof video and active RFID measurements capturing four peoplewearing RFID tags and a couple of passersby. Simulationis then used to evaluate performance for larger numberof targets under different scenarios.identities are of great importance to video analytics applications.For outdoor applications, long range identity sensorssuch as active RFID can provide good coverage in alarge open space, though they only provide coarse locationinformation. We propose a probabilistic approach usingnoisy inputs from multiple long range identity sensorsto globally associate and identify fragmented tracklets generatedby video tracking algorithms. We extend a networkflow based data association model to recover tracklet identityefficiently. Our approach is evaluated using five minutesof video and active RFID measurements capturing four peoplewearing RFID tags and a couple of passersby. Simulationis then used to evaluate performance for larger numberof targets under different scenarios.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"32 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":"115656333","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

Human Motion Change Detection by Hierarchical Gaussian Process Dynamical Model with Particle Filter 基于粒子滤波的分层高斯过程动态模型人体运动变化检测

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.55

Yafeng Yin, H. Man, Jing Wang, Guang Yang

{"title":"Human Motion Change Detection by Hierarchical Gaussian Process Dynamical Model with Particle Filter","authors":"Yafeng Yin, H. Man, Jing Wang, Guang Yang","doi":"10.1109/AVSS.2010.55","DOIUrl":"https://doi.org/10.1109/AVSS.2010.55","url":null,"abstract":"Human motion change detection is a challenging taskfor a surveillance sensor system. Major challenges includecomplex scenes with a large amount of targets and confusors,and complex motion behaviors of different human objects.Human motion change detection and understandinghave been intensively studied over the past decades. In thispaper, we present a Hierarchical Gaussian Process DynamicalModel (HGPDM) integrated with particle filter trackerfor humanmotion change detection. Firstly, the high dimensionalhuman motion trajectory training data is projected tothe low dimensional latent space with a two-layer hierarchy.The latent space at the leaf node in bottom layer representsa typical humanmotion trajectory, while the root node in theupper layer controls the interaction and switching amongleaf nodes. The trained HGPDM will then be used to classifytest object trajectories which are captured by the particlefilter tracker. If the motion trajectory is different fromthe motion in the previous frame, the root node will transferthe motion trajectory to the corresponding leaf node. Inaddition, HGPDM can be used to predict the next motionstate, and provide Gaussian process dynamical samples forthe particle filter framework. The experiment results indicatethat our framework can accurately track and detect thehuman motion changes despite of complex motion and occlusion.In addition, the sampling in the hierarchical latentspace has greatly improved the efficiency of the particle filterframework.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"28 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":"122042661","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: 8

An Activity Monitoring System for Real Elderly at Home: Validation Study 一个真正的居家老人活动监测系统:验证研究

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.83

N. Zouba, F. Brémond, M. Thonnat

引用次数: 58

Robust Dynamic Super Resolution under Inaccurate Motion Estimation 不准确运动估计下的鲁棒动态超分辨率

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.49

Minjae Kim, Bonhwa Ku, Daesung Chung, Hyunhak Shin, Bonghyup Kang, D. Han, Hanseok Ko

引用次数: 5