  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.
