2006 IEEE International Conference on Video and Signal Based Surveillance最新文献_第8页

Sensor Bandwidth Assignment through Video Annotation 基于视频标注的传感器带宽分配

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.102

C. Micheloni, L. Snidaro, Ingrid Visentini, G. Foresti

{"title":"Sensor Bandwidth Assignment through Video Annotation","authors":"C. Micheloni, L. Snidaro, Ingrid Visentini, G. Foresti","doi":"10.1109/AVSS.2006.102","DOIUrl":"https://doi.org/10.1109/AVSS.2006.102","url":null,"abstract":"The state of the art of surveillance systems include a large set of techniques for both low level and high level tasks. In particular, the research community has witnessed in the last decade a high proliferation of techniques that span from object detection and tracking to object recogni- tion and event understanding. Although some techniques have been proven to be very effective those tasks cannot be considered solved. Although more effort is needed in the event analysis field, a new problem arises from the develop- ment of large scale networked surveillance systems: infor- mation sharing. The way information is shared between the nodes of the surveillance network today represents a key- point issue. To provide a first and novel solution to such a problem, we propose an innovative system architecture for a video surveillance system with distributed processing over multiple processing units and with distributed communica- tion over multiple heterogeneous channels (wireless, satel- lite, local IP networks, etc.). In particular, a new real-time technique for changing the video transmission parameters (e.g., frame rate, spatial/color resolution, etc.) according to the bandwidth available will be here presented.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"1 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":"125518433","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

A Validated Method for Dense Non-rigid 3D Face Registration 一种高密度非刚性三维人脸配准的验证方法

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.15

J. Tena, M. Hamouz, A. Hilton, J. Illingworth

引用次数: 42

A Random Field Model for Improved Feature Extraction and Tracking 一种改进特征提取与跟踪的随机场模型

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.11

Xiao-Tong Yuan, S. Li

引用次数: 0

Estimation of Internal and External Parameters for Camera Calibration Using 1D Pattern 基于一维模式的摄像机标定内外参数估计

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.48

Xiangjian He, Huaifeng Zhang, N. Hur, Jinwoong Kim, Qiang Wu, Taeone Kim

引用次数: 15

Generic Object Recognition Using a Combination of ICA and Shape Cues 结合ICA和形状线索的通用物体识别

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.52

Manisha Kalra, Sukhendu Das, A. Datta

引用次数: 3

Measuring Skin Topographic Structures through Capacitance Image Analysis 通过电容图像分析测量皮肤的地形结构

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.74

A. Bevilacqua, A. Gherardi, R. Guerrieri

引用次数: 2

Near- and Far- Infrared Imaging for Vein Pattern Biometrics 静脉模式生物识别的近红外和远红外成像

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.80

Lingyu Wang, G. Leedham

{"title":"Near- and Far- Infrared Imaging for Vein Pattern Biometrics","authors":"Lingyu Wang, G. Leedham","doi":"10.1109/AVSS.2006.80","DOIUrl":"https://doi.org/10.1109/AVSS.2006.80","url":null,"abstract":"This paper investigates two infrared imaging technologies, far-infrared thermography and near-infrared imaging, to acquire hand vein pattern images for biometric purposes. The imaging principles for both technologies are studied in depth. Experiments involving data acquisition from various parts of hand, including the back of the hand, palm, and wrist are described using a population of 150 participants using both near and far infrared imaging techniques. Comparison and analysis of the data collected show that far-infrared thermography has difficulties in capturing vein images in the palm, and wrist. However, while it is more suitable for capturing the large veins in the back of the hand, it is sensitive to ambient conditions and human body condition and does not provide a stable image quality. On the other hand, near-infrared imaging produces good quality images when capturing vein patterns in the back of the hand, palm, and wrist. It is more tolerant to changes in environmental and body condition, but it also faces the problem of disruption due to skin features such as hairs and line patterns. An initial vein pattern biometric system is implemented. The results show that all the test subjects can be correctly identified.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"6 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":"129558112","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: 203

The Role of Motion Models in Super-Resolving Surveillance Video for Face Recognition 运动模型在超分辨监控视频人脸识别中的作用

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.113

Frank Lin, C. Fookes, V. Chandran, S. Sridharan

{"title":"The Role of Motion Models in Super-Resolving Surveillance Video for Face Recognition","authors":"Frank Lin, C. Fookes, V. Chandran, S. Sridharan","doi":"10.1109/AVSS.2006.113","DOIUrl":"https://doi.org/10.1109/AVSS.2006.113","url":null,"abstract":"Although the use of super-resolution techniques has demonstrated the ability to improve face recognition accuracy when compared to traditional upsampling techniques, they are difficult to implement for real-time use due to their complexity and high computational demand. As a large portion of processing time is dedicated to registering the lowresolution images, many have adopted global motion models in order to improve efficiency. The drawback of such global models is that they can not accommodate for complex local motions, such as multiple objects moving independently across and static or dynamic background as frequently occurs in a surveillance environment. Local methods like optical flow can compensate for these situations, although it is achieved at the expense of computation time. In this paper, experiments have been carried out to investigate how motion models of different super-resolution reconstruction algorithms affect reconstruction error and face recognition rates in a surveillance environment. Results show that lower reconstruction error doesn't necessarily imply better recognition rates and the use of local motion models yields better recognition rates than global motion models.","PeriodicalId":289876,"journal":{"name":"2006 IEEE International Conference on Video and Signal Based Surveillance","volume":"30 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":"129583359","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: 5

Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition 二维和三维人脸识别的PCA差分向量特征建模

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.50

C. McCool, Jamie Cook, V. Chandran, S. Sridharan

引用次数: 3

Data Fusion and Confidence Measure in Image Feature Detection 图像特征检测中的数据融合与置信度度量

2006 IEEE International Conference on Video and Signal Based Surveillance Pub Date : 2006-11-22 DOI: 10.1109/AVSS.2006.38

Qiuyu Zhu, Sujuan Huang

引用次数: 0