MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
This work addresses the problem of general-category tracking for computer vision applications, offering a novel approach beyond incremental improvements.
The paper tackles the limitation of existing multi-object tracking systems to specific categories by introducing MOTS, a few-shot method that can track 31 categories and generalize to unseen ones, achieving 88.76% assignments on the MOT16 training set without performance loss.
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance. In the past few years, numerous attempts have been made to perfect the systems. Although they presented favorable performance, they were constrained to track specified category. Drawing on the ideas of few shot method, we pioneered a new multi-target tracking system, named MOTS, which is based on metrics but not limited to track specific category. It contains two stages in series: In the first stage, we design the self-Adaptive-matching module to perform simple targets matching, which can complete 88.76% assignments without sacrificing performance on MOT16 training set. In the second stage, a Fine-match Network was carefully designed for unmatched targets. With a newly built TRACK-REID data-set, the Fine-match Network can perform matching of 31 category targets, even generalizes to unseen categories.