2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance最新文献_第3页

Action Recognition from Experience 从经验中认识行动

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.85

P. Tu, T. Sebastian, Dashan Gao

引用次数: 0

Stereo-Based Framework for Pedestrian Detection with Partial Occlusion Handling 基于立体的部分遮挡行人检测框架

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.71

Samuele Martelli, M. Cristani, Vittorio Murino

引用次数: 1

Discovering Activities and Their Temporal Significance 发现活动及其时间意义

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.37

Ayesha Choudhary, T. Faruquie, Subhashis Banerjee, S. Chaudhury

引用次数: 1

Unsupervised Discovery of Activities and Their Temporal Behaviour 活动的无监督发现及其时间行为

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.79

T. Faruquie, Subhashis Banerjee, P. Kalra

引用次数: 3

Interest Point Selection with Spatio-temporal Context for Realistic Action Recognition 基于时空背景的兴趣点选择在现实动作识别中的应用

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.43

Yanhu Shan, Z. Zhang, Junge Zhang, Kaiqi Huang, Na Wu, Oh Se Hyun

引用次数: 1

Spatio-temporal LBP Based Moving Object Segmentation in Compressed Domain 基于时空LBP的压缩域运动目标分割

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.68

Jianwei Yang, Shizheng Wang, Zhen Lei, Yanyun Zhao, S. Li

引用次数: 11

Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories 基于轨迹的多台固定式激光测距仪自动标定

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.14

Konrad Schenk, Alexander Kolarow, M. Eisenbach, Klaus Debes, H. Groß

{"title":"Automatic Calibration of Multiple Stationary Laser Range Finders Using Trajectories","authors":"Konrad Schenk, Alexander Kolarow, M. Eisenbach, Klaus Debes, H. Groß","doi":"10.1109/AVSS.2012.14","DOIUrl":"https://doi.org/10.1109/AVSS.2012.14","url":null,"abstract":"Laser based detection and tracking of persons can be used for numerous tasks, like statistical measurements for determining bottlenecks in public buildings, optimizing passenger flow, or planning camera placement. Only a network of multiple LRF is sufficient to fulfill these tasks in larger spaces. Calibrating multiple LRF into a global coordinate system is usually done by hand in a time consuming procedure. In this paper, we address the problem of automatically calibrating such a sensor network. We introduce an automatic calibration mechanism, which is able to obtain the positions and orientations of all LRF in a global coordinate system, without any prior knowledge of the scene. Our approach is based on comparing person tracks, determined by each individual LRF unit and matching them in order to obtain constraints between the LRF units. By resolving these constraints, we are able to estimate the poses of all LRF. We evaluate and compare our method to the current state of the art approach methodically and experimentally. Experiments show that our calibration approach outperforms this approach.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"1 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":"125420524","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: 4

Clustering Motion for Real-Time Optical Flow Based Tracking 基于实时光流跟踪的聚类运动

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.20

T. Senst, Rubén Heras Evangelio, I. Keller, T. Sikora

引用次数: 9

Online Learning of Activities from Video 视频活动的在线学习

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.50

J. L. Patino, F. Brémond, M. Thonnat

{"title":"Online Learning of Activities from Video","authors":"J. L. Patino, F. Brémond, M. Thonnat","doi":"10.1109/AVSS.2012.50","DOIUrl":"https://doi.org/10.1109/AVSS.2012.50","url":null,"abstract":"The present work introduces a new method for activity extraction from video. To achieve this, we focus on the modelling of context by developing an algorithm that automatically learns the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Automatically learning the context of the scene (activity zones) allows first to extract a knowledge on the occupancy of the different areas of the scene. In a second step, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones, in this way, the activity of a person can be summarised as the series of zones that the person has visited. For the analysis of the trajectory, a multiresolution analysis is set such that a trajectory is segmented into a series of tracklets based on changing speed points thus allowing differentiating when people stop to interact with elements of the scene or other persons. Tracklets allow thus to extract behavioural information. Starting and ending tracklet points are fed to a simple yet advantageous incremental clustering algorithm to create an initial partition of the scene. Similarity relations between resulting clusters are modeled employing fuzzy relations. These can then be aggregated with typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the final structure of the scene. To allow for incremental learning and update of activity zones (and thus people activities), fuzzy relations are defined with online learning terms. We present results obtained on real videos from different activity domains.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"59 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":"131374263","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

SLTP: A Fast Descriptor for People Detection in Depth Images SLTP:深度图像中人检测的快速描述符

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.67

Shiqi Yu, Shengyin Wu, Liang Wang

引用次数: 13