TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under
Complex Motions and Diverse Scenes
Abstract
Video data and algorithms have been driving advances in multi-object
tracking (MOT). While existing MOT datasets focus on occlusion and
appearance similarity, complex motion patterns are widespread yet
overlooked. To address this issue, we introduce a new dataset called
BEE23 to highlight complex motions. Identity association algorithms have
long been the focus of MOT research. Existing trackers can be
categorized into two association paradigms: single-feature paradigm
(based on either motion or appearance feature) and serial paradigm (one
feature serves as secondary while the other is primary). However, these
paradigms are incapable of fully utilizing different features. In this
paper, we propose a parallel paradigm and present the Two rOund Parallel
matchIng meChanism (TOPIC) to implement it. The TOPIC leverages both
motion and appearance features and can adaptively select the preferable
one as the assignment metric based on motion level. Moreover, we provide
an Attention-based Appearance Reconstruct Module (AARM) to reconstruct
appearance feature embeddings, thus enhancing the representation of
appearance features. Comprehensive experiments show that our approach
achieves state-of-the-art performance on four public datasets and BEE23.
Notably, our proposed parallel paradigm surpasses the performance of
existing association paradigms by a large margin, e.g., reducing false
negatives by 12% to 51% compared to the single-feature association
paradigm. The introduced dataset and association paradigm in this work
offer a fresh perspective for advancing the MOT field. The source code
and dataset are available at https://github.com/holmescao/TOPICTrack.