OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking
<|reference_start|>OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking: We study a novel yet practical problem of open-corpus multi-object tracking (OCMOT), which extends the MOT into localizing, associating, and recognizing generic-category objects of both seen (base) and unseen (novel) classes, but without the category text list as prompt. To study this problem, the top priority is to build a benchmark. In this work, we build OCTrackB, a large-scale and comprehensive benchmark, to provide a standard evaluation platform for the OCMOT problem. Compared to previous datasets, OCTrackB has more abundant and balanced base/novel classes and the corresponding samples for evaluation with less bias. We also propose a new multi-granularity recognition metric to better evaluate the generative object recognition in OCMOT. By conducting the extensive benchmark evaluation, we report and analyze the results of various state-of-the-art methods, which demonstrate the rationale of OCMOT, as well as the usefulness and advantages of OCTrackB.<|reference_end|>
@article{qian2024octrack:,
    title={OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking},
    author={Zekun Qian, Ruize Han, Wei Feng, Junhui Hou, Linqi Song, Song Wang},
    journal={arXiv preprint arXiv:2407.14047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14047},
    primaryClass={cs.CV cs.AI}
}