OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking

• URL: http://arxiv.org/abs/2407.14047v1
• Date: Fri, 19 Jul 2024 05:58:01 GMT
• Title: OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking
• Authors: Zekun Qian, Ruize Han, Wei Feng, Junhui Hou, Linqi Song, Song Wang,
• Abstract summary: We study a novel yet practical problem of open-corpus multi-object tracking (OCMOT) We build OCTrackB, a large-scale and comprehensive benchmark, to provide a standard evaluation platform for the OCMOT problem.
• Score: 63.53176412315835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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