OCTrack：开放语料多目标跟踪基准
OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking
AI总结：
针对开放语料多目标跟踪问题，构建了大规模基准OCTrackB，并提出多粒度识别度量，验证了该问题及基准的有效性。
摘要：
我们研究了一个新颖且实际的问题——开放语料多目标跟踪（OCMOT），该问题将多目标跟踪（MOT）扩展为对已见（基类）和未见（新类）的通用类别目标进行定位、关联和识别，且不依赖类别文本列表作为提示。为了研究该问题，首要任务是构建基准。在本工作中，我们构建了OCTrackB，一个大规模且全面的基准，为OCMOT问题提供标准评估平台。与之前的数据集相比，OCTrackB具有更丰富且更平衡的基类/新类及其对应样本，评估偏差更小。我们还提出了一种新的多粒度识别度量，以更好地评估OCMOT中的生成式目标识别。通过进行广泛的基准评估，我们报告并分析了各种最先进方法的结果，这些结果证明了OCMOT的合理性以及OCTrackB的有效性和优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
