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arXiv · 2407.14047

OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking

Zekun Qian·Ruize Han·Wei Feng·Junhui Hou·Linqi Song·Song Wang
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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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Zekun Qian, Ruize Han, Wei Feng, Junhui Hou, Linqi Song, Song Wang. 2024-07-19. OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking. https://arxiv.org/abs/2407.14047

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