MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Patrick Dendorfer, Aljos̆a Os̆ep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, Laura Leal-Taixé

Research output: Contribution to journalArticlepeer-review

167 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)845-881
Number of pages37
JournalInternational Journal of Computer Vision
Volume129
Issue number4
DOIs
StatePublished - Apr 2021

Keywords

  • Computer vision
  • Evaluation
  • MOTA
  • MOTChallenge
  • Multi-object-tracking

Fingerprint

Dive into the research topics of 'MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking'. Together they form a unique fingerprint.

Cite this