Visual Tracking: An Experimental Survey

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...

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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, 1939-3539
Online Access:	Get full text
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Abstract	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.
AbstractList	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.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. 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.
Author	Dehghan, Afshin Cucchiara, Rita Shah, Mubarak Calderara, Simone Smeulders, Arnold W. M. Chu, Dung M.
Author_xml	– sequence: 1 givenname: Arnold W. M. surname: Smeulders fullname: Smeulders, Arnold W. M. email: arnold.smeulders@cwi.nl organization: Inf. Inst., Univ. of Amsterdam, Amsterdam, Netherlands – sequence: 2 givenname: Dung M. surname: Chu fullname: Chu, Dung M. email: chu@uva.nl organization: Inf. Inst., Univ. of Amsterdam, Amsterdam, Netherlands – sequence: 3 givenname: Rita surname: Cucchiara fullname: Cucchiara, Rita email: rita.cucchiara@unimore.it organization: Fac. of Eng. of Modena, Univ. of Modena & Reggio Emilia, Modena, Italy – sequence: 4 givenname: Simone surname: Calderara fullname: Calderara, Simone email: simone.calderara@unimore.it organization: Fac. of Eng. of Modena, Univ. of Modena & Reggio Emilia, Modena, Italy – sequence: 5 givenname: Afshin surname: Dehghan fullname: Dehghan, Afshin email: afshin.dn@gmail.com organization: Sch. of Electr. Eng. & Comput. Sci., Univ. of Florida, Orlando, FL, USA – sequence: 6 givenname: Mubarak surname: Shah fullname: Shah, Mubarak email: shah@eecs.ucf.edu organization: Sch. of Electr. Eng. & Comput. Sci., Univ. of Florida, Orlando, FL, USA
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Copyright	2015 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jul 2014
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Snippet	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...
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SubjectTerms	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
Title	Visual Tracking: An Experimental Survey
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