2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance最新文献_第5页

Incremental Learning Approach for Events Detection from Large Video Dataset 大型视频数据集事件检测的增量学习方法

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.54

A. Wali, A. Alimi

引用次数: 16

An Authentication Mechanism Using Chinese Remainder Theorem for Efficient Surveillance Video Transmission 基于中国剩余定理的高效监控视频传输认证机制

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.13

Tony Thomas, S. Emmanuel, Peng Zhang, M. Kankanhalli

引用次数: 4

Example-Based Color Vehicle Retrieval for Surveillance 基于实例的彩色车辆监控检索

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.59

L. Brown

引用次数: 22

Exploiting Geometric Restrictions in a PTZ Camera for Finding Point-orrespondences Between Configurations 利用PTZ相机的几何限制寻找构型之间的点对应

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.53

Birgi Tamersoy, J. Aggarwal

{"title":"Exploiting Geometric Restrictions in a PTZ Camera for Finding Point-orrespondences Between Configurations","authors":"Birgi Tamersoy, J. Aggarwal","doi":"10.1109/AVSS.2010.53","DOIUrl":"https://doi.org/10.1109/AVSS.2010.53","url":null,"abstract":"A pan-tilt-zoom (PTZ) camera, fixed in location, mayperform only rotational movements. There is a class offeature-based self-calibration approaches that exploit therestrictions on the camera motion in order to obtain accuratepoint-correspondences between two configurations ofa PTZ camera. Most of these approaches require extensivecomputation and yet do not guarantee a satisfactory result.In this paper, we approach this problem from a differentperspective. We exploit the geometric restrictions on the imageplanes, which are imposed by the motion restrictions onthe camera. We present a simple method for estimating thecamera focal length and finding the point-correspondencesbetween two camera configurations. We compute pan-only,tilt-only and zoom-only correspondences and then combinethe three to derive the geometrical relationship between anytwo camera configurations. We perform radial lens distortionestimation in order to calibrate distorted image coordinates.Our purely geometric approach does not require anyintensive computations, feature tracking or training. However,our point-correspondence experiments show that, itstill performs well-enough for most computer vision applicationsof PTZ cameras.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"21 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":"125209216","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: 0

Dynamic Sensor Selection for Single Target Tracking in Large Video Surveillance Networks 大型视频监控网络中单目标跟踪的动态传感器选择

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.22

Eduardo Monari, K. Kroschel

引用次数: 6

Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow 基于稀疏表示的光流协方差流形动作识别

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.71

Kai Guo, P. Ishwar, J. Konrad

{"title":"Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow","authors":"Kai Guo, P. Ishwar, J. Konrad","doi":"10.1109/AVSS.2010.71","DOIUrl":"https://doi.org/10.1109/AVSS.2010.71","url":null,"abstract":"A novel approach to action recognition in video based onthe analysis of optical flow is presented. Properties of opticalflow useful for action recognition are captured usingonly the empirical covariance matrix of a bag of featuressuch as flow velocity, gradient, and divergence. The featurecovariance matrix is a low-dimensional representationof video dynamics that belongs to a Riemannian manifold.The Riemannian manifold of covariance matrices is transformedinto the vector space of symmetric matrices underthe matrix logarithm mapping. The log-covariance matrixof a test action segment is approximated by a sparse linearcombination of the log-covariance matrices of training actionsegments using a linear program and the coefficients ofthe sparse linear representation are used to recognize actions.This approach based on the unique blend of a logcovariance-descriptor and a sparse linear representation istested on the Weizmann and KTH datasets. The proposedapproach attains leave-one-out cross validation scores of94.4% correct classification rate for the Weizmann datasetand 98.5% for the KTH dataset. Furthermore, the methodis computationally efficient and easy to implement.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"30 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":"128747275","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: 176

Fast People Counting Using Head Detection from Skeleton Graph 基于骷髅图的头部检测快速计数

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.91

D. Merad, Kheir-Eddine Aziz, Nicolas Thome

引用次数: 50

Video Activity Extraction and Reporting with Incremental Unsupervised Learning 基于增量无监督学习的视频活动提取与报告

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.74

J. L. Patino, F. Brémond, M. Evans, Ali Shahrokni, J. Ferryman

引用次数: 12

Tracking People with a 360-Degree Lidar 用360度激光雷达跟踪人

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.52

J. Shackleton, B. V. Voorst, Joel A. Hesch

引用次数: 70

Recognizing and Localizing Individual Activities through Graph Matching 基于图匹配的个体活动识别与定位

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI: 10.1109/AVSS.2010.81

Anh-Phuong Ta, Christian Wolf, G. Lavoué, A. Baskurt

{"title":"Recognizing and Localizing Individual Activities through Graph Matching","authors":"Anh-Phuong Ta, Christian Wolf, G. Lavoué, A. Baskurt","doi":"10.1109/AVSS.2010.81","DOIUrl":"https://doi.org/10.1109/AVSS.2010.81","url":null,"abstract":"In this paper we tackle the problem of detecting individualhuman actions in video sequences. While the most successfulmethods are based on local features, which proved thatthey can deal with changes in background, scale and illumination,most existing methods have two main shortcomings:first, they are mainly based on the individual power ofspatio-temporal interest points (STIP), and therefore ignorethe spatio-temporal relationships between them. Second,these methods mainly focus on direct classification techniquesto classify the human activities, as opposed to detectionand localization. In order to overcome these limitations,we propose a new approach, which is based on agraph matching algorithm for activity recognition. In contrastto most previous methods which classify entire videosequences, we design a video matching method from twosets of ST-points for human activity recognition. First,points are extracted, and a hyper graphs are constructedfrom them, i.e. graphs with edges involving more than 2nodes (3 in our case). The activity recognition problemis then transformed into a problem of finding instances ofmodel graphs in the scene graph. By matching local featuresinstead of classifying entire sequences, our methodis able to detect multiple different activities which occursimultaneously in a video sequence. Experiments on twostandard datasets demonstrate that our method is comparableto the existing techniques on classification, and that itcan, additionally, detect and localize activities.","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"92 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":"124225691","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: 32