Spatiotemporal BEV Feature Enhancement for Multi-Target Multi-Camera Tracking

This paper proposes a fusion network based on spatiotemporal BEV feature enhancement, aiming to strengthen the representation capability of spatiotemporal BEV information in global contexts. The network employs a spatial BEV feature enhancement module to deeply integrate individual BEV features with the initial global BEV features obtained from the stacking and aggregation of all BEV features, thereby generating guided BEV features with enhanced representational capabilities. These features are then re-stacked and aggregated to form a refined global BEV feature. Additionally, a temporal BEV feature enhancement module is introduced to fuse historical and current BEV features, expanding the receptive field to address the association challenges posed by rapidly moving targets. Experimental results demonstrated that this enhanced architecture achieves state-of-the-art performance on the Wildtrack and MultiviewX datasets, with MODA reaching 92.3% and 96.5%, and IDF1 scores reaching 95.0% and 84.8%, respectively. The results validated the effectiveness of our method from multiple perspectives.