2006 IEEE International Conference on Video and Signal Based Surveillance最新文献_第6页

An Online Discriminative Approach to Background Subtraction 背景减法的在线判别方法

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.22

Li Cheng, Shaojun Wang, Dale Schuurmans, T. Caelli, S. Vishwanathan

引用次数: 20

Template Matching Based on the L_p Norm Using Sufficient Conditions with Incremental Approximations 基于L_p范数的模板匹配与增量逼近的充分条件

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.110

Federico Tombari, S. Mattoccia, L. D. Stefano

引用次数: 6

A Novel Statistical Model for Speech Recognition and POS Tagging 一种新的语音识别和词性标注统计模型

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.9

Lichi Yuan, Zhigang Chen

引用次数: 2

Shape Retrieval Using Matching Pursuit Decomposition 基于匹配追踪分解的形状检索

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.103

Binhai Wang, J. Bangham

引用次数: 3

Group Detection at Camera Handoff for Collecting People Appearance in Multi-camera Systems 多摄像机系统中采集人物外貌的切换组检测

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.55

S. Calderara, R. Cucchiara, A. Prati

引用次数: 6

Video Surveillance at the Beginning of the Third Millennium: The Viewpoint of Research, Industry, Government Bodies, Research Funding Agencies and the Community 第三个千年之初的视频监控:研究、工业、政府机构、研究资助机构和社区的观点

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.115

M. Piccardi

引用次数: 0

Optimal Power Scheduling for Data Fusion in Inhomogeneous Wireless Sensor Networks 非均匀无线传感器网络中数据融合的最优功率调度

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.89

T. Wimalajeewa, S. Jayaweera

引用次数: 8

Support Vector Machine with Weighted Summation Kernel Obtained by Adaboost Adaboost获得的加权求和核支持向量机

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.109

K. Hotta

{"title":"Support Vector Machine with Weighted Summation Kernel Obtained by Adaboost","authors":"K. Hotta","doi":"10.1109/AVSS.2006.109","DOIUrl":"https://doi.org/10.1109/AVSS.2006.109","url":null,"abstract":"This paper presents Support Vector Machine (SVM) with weighted summation kernel obtained by Adaboost. In recent years, SVM with local summation kernel is proposed to use local features in SVM effectively. However, the computational cost of original method is high because all local kernels are used. In general, the effective position and size are different for recognition task. However, original method applied local kernel with same size to all scalar features and all local kernels are integrated with equal weight. To improve the performance and reduce the computational cost, local kernels with various size are prepared at all positions of a recognition target and effective local kernels are selected by Adaboost. Since 1-Nearest Neighbor (1-NN) of the output of local Gaussian kernel at certain size and position is used as weak learner, a strong classifier obtained by Adaboost becomes a new weighted summation kernel specialized for given recognition task. The proposed method is applied to face detection task. We confirmed that the proposed weighted summation kernel gives better performance than original local summation kernel though the proposed method uses the smaller number of local kernels than local summation kernel.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122815859","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

Classification of Unattended and Stolen Objects in Video-Surveillance System 视频监控系统中无人看管和被盗物品的分类

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.32

Silvia Ferrando, G. Gera, C. Regazzoni

引用次数: 64

Mitigating the Effects of Variable Illumination for Tracking across Disjoint Camera Views 减轻在不相交的相机视图中跟踪的可变照明的影响

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.76

E. Cheng, Christopher S. Madden, M. Piccardi

{"title":"Mitigating the Effects of Variable Illumination for Tracking across Disjoint Camera Views","authors":"E. Cheng, Christopher S. Madden, M. Piccardi","doi":"10.1109/AVSS.2006.76","DOIUrl":"https://doi.org/10.1109/AVSS.2006.76","url":null,"abstract":"Tracking people by their appearance across disjoint camera views is challenging since appearance may vary significantly across such views. This problem has been tackled in the past by computing intensity transfer functions between each camera pair during an initial training stage. However, in real-life situations, intensity transfer functions depend not only on the camera pair, but also on the actual illumination at pixel-wise resolution and may prove impractical to estimate to a satisfactory extent. For this reason, in this paper we propose an appearance representation for people tracking capable of coping with the typical illumination changes occurring in a surveillance scenario. Our appearance representation is based on an online K-means color clustering algorithm, a fixed, data-dependent intensity transformation, and the incremental use of frames. Moreover, a similarity measurement is proposed to match the appearance representations of any two given moving objects along sequences of frames. Experimental results presented in this paper show that the proposed methods provides a viable while effective approach for tracking people across disjoint camera views in typical surveillance scenarios.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128778663","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