Joint feature representation optimization and anti-occlusion for robust multi-vessel tracking in inland waterways

Multiple vessel tracking plays a vital role in maritime surveillance systems. Previous studies have typically integrated object detection and trajectory association techniques to address this problem, but they still face some significant challenges. On one hand, these methods are susceptible to losing tracked targets due to long-term occlusion by other obstacles or slow-moving vessels in inland waterways. Moreover, traditional models encounter difficulties in accurately capturing the global appearance features of the vessels in images, which leads to a decline in vessel detection performance. To address the issues above, this paper proposes a novel Vessel Status Augmented Track (VSATrack) framework for multi-vessel detection and tracking. Specifically, we present a Motion-Matching Optimization Module (MMOM), which handles long-term occlusion through identity matching between consecutive frames. Besides, a vessel feature enhancement module (VFEM) with several residual convolutional layers and channel reconstruction units (CRU) is designed to effectively capture the vessels features in complex inland waterway backgrounds without introducing redundant channel information. Finally, a bidirectional feature pyramid network (BiFPN) is utilized to fuse vessel appearance features from different scales, enhancing the capability to learn cross-scale features of vessels to some extent. Experimental results demonstrate that our VSATrack method outperforms the state-of-the-art methods, particularly in reducing the number of vessel ID switches (IDSW).