A method for performance diagnosis and evaluation of video trackers

A method for performance diagnosis and evaluation of video trackers Several measures for evaluating multi-target video trackers exist that generally aim at providing ‘end performance.’ End performance is important particularly for ranking and comparing trackers. However, for a deeper insight into trackers’ performance it would also be desirable to analyze key contributory factors (false positives, false negatives, ID changes) that (implicitly or explicitly) lead to the attainment of a certain end performance. Specifically, this paper proposes a new approach to enable a diagnosis of the performance of multi-target trackers as well as providing a means to determine the end performance to still enable their comparison in a video sequence. Diagnosis involves analyzing probability density functions of false positives, false negatives and ID changes of trackers in a sequence. End performance is obtained in terms of the extracted performance scores related to false positives, false negatives and ID changes. In the experiments, we used four state-of-the-art trackers on challenging real-world public datasets to show the effectiveness of the proposed approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Signal, Image and Video Processing" Springer Journals

A method for performance diagnosis and evaluation of video trackers

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Publisher
Springer London
Copyright
Copyright © 2017 by The Author(s)
Subject
Engineering; Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems
ISSN
1863-1703
eISSN
1863-1711
D.O.I.
10.1007/s11760-017-1086-7
Publisher site
See Article on Publisher Site

Abstract

Several measures for evaluating multi-target video trackers exist that generally aim at providing ‘end performance.’ End performance is important particularly for ranking and comparing trackers. However, for a deeper insight into trackers’ performance it would also be desirable to analyze key contributory factors (false positives, false negatives, ID changes) that (implicitly or explicitly) lead to the attainment of a certain end performance. Specifically, this paper proposes a new approach to enable a diagnosis of the performance of multi-target trackers as well as providing a means to determine the end performance to still enable their comparison in a video sequence. Diagnosis involves analyzing probability density functions of false positives, false negatives and ID changes of trackers in a sequence. End performance is obtained in terms of the extracted performance scores related to false positives, false negatives and ID changes. In the experiments, we used four state-of-the-art trackers on challenging real-world public datasets to show the effectiveness of the proposed approach.

Journal

"Signal, Image and Video Processing"Springer Journals

Published: Mar 30, 2017

References

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