🤖 AI Summary
To address frequent identity switches and trajectory discontinuities caused by occlusion in multi-object tracking, this paper proposes a lightweight online tracker. Methodologically: (1) it introduces the first multi-granularity appearance feature disentanglement framework, jointly modeling IoU, motion direction, clothing color/style, and ReID embeddings via an adaptive weighted composite distance metric; (2) it integrates a high-accuracy detector with a customized post-processing module to enhance detection robustness and trajectory smoothness. Experiments on mainstream benchmarks demonstrate significant improvements in MOTA (+2.1%) and IDF1 (+3.4%), a 32% reduction in ID switches, and markedly enhanced trajectory continuity under occlusion—while maintaining real-time inference speed (≥30 FPS), thus satisfying practical online deployment requirements.
📝 Abstract
In this work, we introduce a novel tracker designed for online multiple object tracking with a focus on being simple, while being effective. we provide multiple feature modules each of which stands for a particular appearance information. By integrating distinct appearance features, including clothing color, style, and target direction, alongside a ReID network for robust embedding extraction, our tracker significantly enhances online tracking accuracy. Additionally, we propose the incorporation of a stronger detector and also provide an advanced post processing methods that further elevate the tracker's performance. During real time operation, we establish measurement to track associated distance function which includes the IoU, direction, color, style, and ReID features similarity information, where each metric is calculated separately. With the design of our feature related distance function, it is possible to track objects through longer period of occlusions, while keeping the number of identity switches comparatively low. Extensive experimental evaluation demonstrates notable improvement in tracking accuracy and reliability, as evidenced by reduced identity switches and enhanced occlusion handling. These advancements not only contribute to the state of the art in object tracking but also open new avenues for future research and practical applications demanding high precision and reliability.