FairMOT-X: Real-time One-shot Methods For Multi-class Multi-object Tracking
Jonathan Tan, Lyuyu Shen, Marcelo H. Ang · 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV) · 2022
Multi-Object Tracking (MOT) is an important task in computer vision. Most research works on MOT focus on tracking multiple objects of a single category, such as humans. However, many applications, such as wildlife surveillance and autonomous driving, require multi-object trackers to track multiple categories of objects. In this work, the extension of state-of-the-art one-shot MOT methods for multi-class tracking is shown to be non-trivial, yielding deteriorated tracking performance due to the complexity of balancing the jointly learned detection and association tasks. To tackle this limitation, a simple improvement to FairMOT is proposed, which replaces the detection head with the decoupled head from YOLOX. The simplified multi-class detection framework yields improved detection and tracking results on the BDD100K MOT validation dataset, yielding an mHOTA of 25.3% compared to 19.7%.