MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

By Patrick Dendorfer, Aljosa Osep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian David Reid, Stefan Roth, L. Leal-Taix'e. International Journal of Computer Vision, 2020

371 citations recorded by the source corpus.

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

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/8a193e0ee60efb931054c35866491ed0f08c55bd

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Citations in the corpus

TrackFormer: Multi-Object Tracking with Transformers (2021, background): “Recent works [4,6,28,66] suggest a variation of the traditional paradigm, coined tracking-by-regression [12].”
Learning Target Candidate Association to Keep Track of What Not to Track (2021, background): “Furthermore, MOT typically focuses on a limited set of object classes [17], such as pedestrians, where strong object detectors are available.”