MOT16

MOT16: A Benchmark for Multi-Object Tracking. Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.


References in zbMATH (referenced in 10 articles )

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  1. Luo, Wenhan; Xing, Junliang; Milan, Anton; Zhang, Xiaoqin; Liu, Wei; Kim, Tae-Kyun: Multiple object tracking: a literature review (2021)
  2. Ma, Cong; Yang, Fan; Li, Yuan; Jia, Huizhu; Xie, Xiaodong; Gao, Wen: Deep human-interaction and association by graph-based learning for multiple object tracking in the wild (2021)
  3. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)
  4. Sven Kreiss, Lorenzo Bertoni, Alexandre Alahi: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association (2021) arXiv
  5. Weis, Marissa A.; Chitta, Kashyap; Sharma, Yash; Brendel, Wieland; Bethge, Matthias; Geiger, Andreas; Ecker, Alexander S.: Benchmarking unsupervised object representations for video sequences (2021)
  6. Ranjan, Anurag; Hoffmann, David T.; Tzionas, Dimitrios; Tang, Siyu; Romero, Javier; Black, Michael J.: Learning multi-human optical flow (2020)
  7. Zheng, Xinye; Ye, Jianbo; Wang, James Z.; Li, Jia: Scott: shape-location combined tracking with optimal transport (2020)
  8. Rouabhia, Houssem Eddine; Farou, Brahim; Kouahla, Zine Eddine; Seridi, Hamid; Akdag, Herman: Cooperative processing based on posture change detection and trajectory estimation for unknown multi-object tracking (2019)
  9. Safaei, Amin; Wu, Q. M. Jonathan; Yang, Yimin: System-on-a-chip (SoC)-based hardware acceleration for foreground and background identification (2018)
  10. Kuplyakov, D.; Shalnov, E.; Konushin, A.: Markov chain Monte Carlo based video tracking algorithm (2017)