{Changick Kim Jubi Hwang Kangwook Ko Kyujin Shim}

Abstract

Multi-object tracking (MOT) plays a pivotal role in various computer vision domains with recent tracking-by detection algorithms that treat MOT as distinct detection and association tasks. However, the existing trackers often rely on sensitive thresholds to associate previous tracks and current detection results while forming complete trajectories across a video. These thresholds are crucial for tracking performance and require manual tuning for each dataset or even sequence, limiting the adaptability in real-world applications. To tackle this problem, in this paper, we introduce AdapTrack, a novel MOT algorithm designed to enable adaption on varying scenarios without handcrafted threshold configuration. With a carefully designed matching strategy, our tracker can adaptively select proper thresholds for each frame and correctly associate detected objects. Consequently, AdapTrack shows outperforming results on standard MOT benchmarks, MOT17 and MOT20, compared to existing state-of-the-art methods. Every source code is available at https://github.com/kamkyu94/AdapTrack.