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A method for performance diagnosis and evaluation of video trackers

Nawaz, T., Ellis, A. and Ferryman, J. (2017) A method for performance diagnosis and evaluation of video trackers. Signal, Image and Video Processing, 11 (7). pp. 1287-1295. ISSN 1863-1703

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To link to this item DOI: 10.1007/s11760-017-1086-7


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:70091


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