A Novel Implicit‐to‐Explicit Association Framework for Multi‐Object Tracking in Radar Video Sequences

Multi‐object tracking (MOT) is a fundamental task in radar technology. Leveraging the visual attributes in radar video data provides an effective approach to enhance tracking robustness, particularly in long‐term scenarios where maintaining track continuity is challenging. However, effectively utilising the visual features of targets in radar videos also faces many limitations, primarily due to inter‐frame jitter in target positions and limited unstable nature of target appearances. To overcome these limitations, we propose a novel association framework for multi‐object tracking in radar video sequences. This framework achieves robust tracking through an implicit‐to‐explicit association strategy: An implicit association neural network (IANN) first establishes implicit associations between detections across consecutive frames end‐to‐end while performing detection, which are subsequently transformed into explicit associations by an explicit association module. Experimental results on real radar video data demonstrate the framework's effectiveness and superior performance. The proposed method stablishes a new benchmark for future research in multi‐object tracking for radar video sequences.