Visual Object Tracking Performance Measures Revisited

被引:139
作者
Cehovin, Luka [1 ]
Leonardis, Ales [1 ,2 ]
Kristan, Matej [1 ]
机构
[1] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana 1000, Slovenia
[2] Univ Birmingham, Sch Comp Sci, Ctr Computat Neurosci & Cognit Robot, Birmingham B15 2TT, W Midlands, England
关键词
Visual object tracking; performance evaluation; performance measures; experimental evaluation;
D O I
10.1109/TIP.2016.2520370
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased toward particular tracking aspects. In this paper, we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis, we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing toward homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent visual object tracking challenges as the foundation for the evaluation methodology.
引用
收藏
页码:1261 / 1274
页数:14
相关论文
共 53 条
[1]  
Adam A., 2006, IEEE C COMPUTER VISI, V1, P798, DOI [DOI 10.1109/CVPR.2006.256, 10.1109/CVPR.2006.256]
[2]  
Alpert S, 2007, PROC CVPR IEEE, P359
[3]  
[Anonymous], 2006, BMVC06
[4]  
[Anonymous], 2012, PROC CVPR IEEE
[5]   Robust Object Tracking with Online Multiple Instance Learning [J].
Babenko, Boris ;
Yang, Ming-Hsuan ;
Belongie, Serge .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (08) :1619-1632
[6]   A Database and Evaluation Methodology for Optical Flow [J].
Baker, Simon ;
Scharstein, Daniel ;
Lewis, J. P. ;
Roth, Stefan ;
Black, Michael J. ;
Szeliski, Richard .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2011, 92 (01) :1-31
[7]  
Bashir F., 2006, Proceedings 9th IEEE International Workshop on PETS, P7
[8]  
Black J., 2003, In Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance VS-PETS, P125
[9]  
Brown L.M., 2005, Proceedings of IEEEPETS Workshop, P1
[10]   Filling the gap in quality assessment of video object tracking [J].
Carvalho, Pedro ;
Cardoso, Jaime S. ;
Corte-Real, Luis .
IMAGE AND VISION COMPUTING, 2012, 30 (09) :630-640