Detection-aware multi-object tracking evaluation

• URL: http://arxiv.org/abs/2212.08536v1
• Date: Fri, 16 Dec 2022 15:35:34 GMT
• Title: Detection-aware multi-object tracking evaluation
• Authors: Juan C. SanMiguel, Jorge Mu\~noz, Fabio Poiesi
• Abstract summary: We propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM can quantify the effort done by the tracker with a reduced correlation on the input detections.
• Score: 1.7880586070278561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.

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