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Standardized benchmarks have been crucial in pushing the performance ofcomputer vision algorithms, especially since the advent of deep learning.Although leaderboards should not be over-claimed, they often provide the mostobjective measure of performance and are therefore important guides forresearch. We present MOTChallenge, a benchmark for single-camera MultipleObject Tracking (MOT) launched in late 2014, to collect existing and new data,and create a framework for the standardized evaluation of multiple objecttracking methods. The benchmark is focused on multiple people tracking, sincepedestrians are by far the most studied object in the tracking community, withapplications ranging from robot navigation to self-driving cars. This papercollects the first three releases of the benchmark: (i) MOT15, along withnumerous state-of-the-art results that were submitted in the last years, (ii)MOT16, which contains new challenging videos, and (iii) MOT17, that extendsMOT16 sequences with more precise labels and evaluates tracking performance onthree different object detectors. The second and third release not only offersa significant increase in the number of labeled boxes but also provide labelsfor multiple object classes beside pedestrians, as well as the level ofvisibility for every single object of interest. We finally provide acategorization of state-of-the-art trackers and a broad error analysis. Thiswill help newcomers understand the related work and research trends in the MOTcommunity, and hopefully shed some light on potential future researchdirections.

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking