Enhanced Global Context Fusion for Multi-Target Multi-Camera Tracking in the Bird's Eye View

Bird's Eye View (BEV) representation learning has greatly improved target detection and tracking in complex environments and autonomous systems, thanks to a multi-view aggregation strategy that enhances spatial awareness, reduces occlusions, and ensures continuous surveillance of targets. This paper presents an Enhanced Global Context Fusion Network for multi-target multi-camera tracking (MTMC), addressing the critical challenge of spatial association across views in large-scale surveillance systems, unlike conventional approaches that directly aggregate Bird's Eye View (BEV) features, our method hierarchically refines individual BEV embedding by integrating global context, generating bootstrap features that better preserve spatial relationships and reduce information loss. In order to improve the problem of poor robustness to real-world issues that may be caused by camera malfunctions, we introduce a data augmentation strategy that replaces the input of lost camera signals with black images during training. The results of extensive experiments on the Wildtrack dataset demonstrate the state-of-the-art performance of our approach, reaching 92.0% for MODA and 94.1% for IDF1, outperforming existing techniques with only a negligible increase in computational cost. These results validate the effectiveness of our approach in terms of improved BEV feature representation and system stability, providing a reliable solution for reliable MTMC detection and tracking in complex environments.