Evaluation of statistical and multiple-hypothesis tracking for video traffic surveillance
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- 作者：Jeffrey E. Boyd and Jean Meloche
- 关键词：Key words ; Tracking – ; Statistics – ; Network tomography – ; Statistical methods – ; Video – ; Motion
- 刊名：Machine Vision and Applications
- 出版年：2003
- 出版时间：March 2003
- 年：2003
- 卷：13
- 期：5-6
- 页码：344-351
- 全文大小：234 KB
- 刊物类别：Computer Science
- 刊物主题：Pattern Recognition
Image Processing and Computer Vision
Communications Engineering and Networks - 出版者：Springer Berlin / Heidelberg
- ISSN：1432-1769
文摘
Abstract. Conventional tracking methods encounter difficulties as the number of objects, clutter, and sensors increase, because of the requirement for data association. Statistical tracking, based on the concept of network tomography, is an alternative that avoids data association. It estimates the number of trips made from one region to another in a scene based on interregion boundary traffic counts accumulated over time. It is not necessary to track an object through a scene to determine when an object crosses a boundary. This paper describes statistical tracing and presents an evaluation based on the estimation of pedestrian and vehicular traffic intensities at an intersection over a period of 1 month. We compare the results with those from a multiple-hypothesis tracker and manually counted ground-truth estimates.