Corpus ID: 214605627

MOT20: A benchmark for multi object tracking in crowded scenes

@article{Dendorfer2020MOT20AB,
  title={MOT20: A benchmark for multi object tracking in crowded scenes},
  author={Patrick Dendorfer and Hamid Rezatofighi and Anton Milan and Javen Shi and Daniel Cremers and Ian Reid and Stefan Roth and Konrad Schindler and Laura Leal-Taix{\'e}},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.09003}
}
  • Patrick Dendorfer, Hamid Rezatofighi, +6 authors Laura Leal-Taixé
  • Published 2020
  • Computer Science
  • ArXiv
  • 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 research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-4 OF 4 CITATIONS

    Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events

    VIEW 6 EXCERPTS
    CITES BACKGROUND
    HIGHLY INFLUENCED

    Object Tracking for Autonomous Driving Systems

    VIEW 3 EXCERPTS
    CITES BACKGROUND & METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 19 REFERENCES

    Evaluating Multi-Object Tracking

    Learning to associate: HybridBoosted multi-target tracker for crowded scene

    VIEW 2 EXCERPTS

    Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph

    VIEW 1 EXCERPT

    Challenges of Ground Truth Evaluation of Multi-target Tracking

    VIEW 1 EXCERPT

    Robust Online Multi-object Tracking Based on Tracklet Confidence and Online Discriminative Appearance Learning

    VIEW 1 EXCERPT

    The CLEAR 2006 Evaluation

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection

    • Bo Wu, Ramakant Nevatia
    • Computer Science
    • 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
    • 2006
    VIEW 3 EXCERPTS