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Benchmark datasets for detection and tracking

Ellis, A.-L. and Ferryman, J. (2011) Benchmark datasets for detection and tracking. In: Moeslund, T. B., Hilton, A., Krüger, V. and Sigal, L. (eds.) Visual Analysis of Humans: Looking at People. Springer, pp. 109-128. ISBN 9780857299963

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To link to this item DOI: 10.1007/978-0-85729-997-0_7


There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:24381

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