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Moving Target Classification and Tracking from Real-time Video
A. Lipton, H. Fujiyoshi, and R. Patil
Proc. of the 1998 DARPA Image Understanding Workshop (IUW'98), November, 1998.

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Abstract

This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to image-based properties, and then robustly tracking them. Moving targets are detected using the pixelwise difference between consecutive image frames. A classificatoin metric is applied these targets with a temporal consistency constraint to classify them into three categories: human, vehicle or background clutter. Once classified, targets are tracked by a combination of temporal differencing and template matching. The resulting system robustly identifies targets of interest, rejects background clutter, and continually tracks over large distances and periods of time despite occlusions, appearance changes and cessation of target motion.

Notes

Associated center: VASC
Associated labs/groups: People Image Analysis Consortium and Video Surveillance and Monitoring
Associated project: Video Surveillance and Monitoring

Text Reference

A. Lipton, H. Fujiyoshi, and R. Patil, "Moving Target Classification and Tracking from Real-time Video," Proc. of the 1998 DARPA Image Understanding Workshop (IUW'98), November, 1998.

BibTeX Reference

@inproceedings{Lipton_1998_2142,
   author = "Alan Lipton and Hironobu Fujiyoshi and Raju Patil",
   title = "Moving Target Classification and Tracking from Real-time Video",
   booktitle = "Proc. of the 1998 DARPA Image Understanding Workshop (IUW'98)",
   month = "November",
   year = "1998"
}