  Multi-object tracking (MOT) endeavors to precisely estimate the positions and
identities of multiple objects over time. The prevailing approach,
tracking-by-detection (TbD), first detects objects and then links detections,
resulting in a simple yet effective method. However, contemporary detectors may
occasionally miss some objects in certain frames, causing trackers to cease
tracking prematurely. To tackle this issue, we propose BUSCA, meaning `to
search', a versatile framework compatible with any online TbD system, enhancing
its ability to persistently track those objects missed by the detector,
primarily due to occlusions. Remarkably, this is accomplished without modifying
past tracking results or accessing future frames, i.e., in a fully online
manner. BUSCA generates proposals based on neighboring tracks, motion, and
learned tokens. Utilizing a decision Transformer that integrates multimodal
visual and spatiotemporal information, it addresses the object-proposal
association as a multi-choice question-answering task. BUSCA is trained
independently of the underlying tracker, solely on synthetic data, without
requiring fine-tuning. Through BUSCA, we showcase consistent performance
enhancements across five different trackers and establish a new
state-of-the-art baseline across three different benchmarks. Code available at:
https://github.com/lorenzovaquero/BUSCA.
