期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 36, 期 7, 页码 1442-1468出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2013.230
关键词
Object tracking; tracking evaluation; tracking dataset; camera surveillance; video understanding; computer vision; image processing
资金
- COMMIT
- National Dutch Program for public private ICT research in the Netherlands
- region of Emilia Romagna Italy [EU FESR 2008 15]
- U.S. Army Research Laboratory
- U.S. Army Research Office [W911NF-09-1-0255]
There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.
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