Visual Tracking: An Experimental Survey

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 7; pp. 1442 - 1468
• Main Authors: Smeulders, Arnold W. M., Chu, Dung M., Cucchiara, Rita, Calderara, Simone, Dehghan, Afshin, Shah, Mubarak
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.07.2014; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial intelligence; Camera surveillance; Computer science; control theory; systems; Computer vision; Educational institutions; Exact sciences and technology; Image processing; Object tracking; Pattern recognition. Digital image processing. Computational geometry; Radar tracking; Robustness; Target tracking; Tracking dataset; Tracking evaluation; Video understanding; Videos; video understanding; image processing; tracking evaluation; computer vision; camera surveillance; Object tracking; tracking dataset; Occlusion; Mobile phone; Computer vision; Target tracking; Tracking; Motion estimation; Image processing; Video signal; Mobility; Clutter; Survival; Statistical test; Luminance; Surveillance; Illumination; Occultation
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2013.230

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.