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.

翻译：我们研究了一个新颖且实用的开放语料多目标跟踪问题，该问题将传统MOT扩展为对已见类和未见类通用类别物体的定位、关联与识别，且无需类别文本列表作为提示。为研究此问题，首要任务是构建基准测试集。本工作构建了OCTrackB，一个大规模综合性基准测试集，为OCMOT问题提供标准化评估平台。相较于现有数据集，OCTrackB具有更丰富均衡的基类/新类别别及对应样本，评估偏差更小。我们还提出了一种新的多粒度识别度量标准，以更好地评估OCMOT中的生成式物体识别。通过开展广泛的基准评估，我们报告并分析了多种前沿方法的结果，这些结果验证了OCMOT的合理性，以及OCTrackB的实用价值与优势。