A method for performance diagnosis and evaluation of video trackers

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.

A Method for Tracking Road Objects

“…The evaluation is made through the calculation of the rates of Centroid Error [22] [18] [15] with regard to Ground-Truth (GT) of parts in both sequences (4 parts for 'HighwayII' and 5 parts for HighwayIII). The Centroid Error rates are computed by the Euclidean distance (between two centroids) according to a twopass matching scheme: the first pass matches the system track to GT (distanceSy) to find false positive tracks , and the second pass matches the GT to system track (distanceTrack) to find false negative tracks.…”