Sliding Window Check: Repairing Object Identities

Real-time Multiple Object Tracking (MOT) solutions are popular due to their potential in various tasks. Most real-time oriented solutions use a single model for detection and tracking, and perform pairwise comparisons using past and present features to track objects in time. We find that using a single frame of data is not robust. Some algorithms learn appearance data by relying on additional networks, leading to structures unsuited for real-time performance. By storing multiple past features with a temporal sliding window and performing multiple optimized pairwise comparisons using the window, we are able to improve tracking performance at a small computational overhead. We do so by introducing a novel repair step to the association algorithm. We propose Sliding Window Check (SWC), a tracking algorithm which can be applied to many state-of-the-art one-shot trackers with consistent improvements to HOTA and IDF1 on MOT17 and MOT20. We also address the lack of track-repair algorithms, and concerns about MOTA penalizing track-repairing algorithms. Comparisons against 4 recent works show the efficacy of SWC.