Multi Target Multi Camera Tracking

Multi-target multi-camera tracking (MTMCT) aims to accurately track multiple objects across multiple cameras, a crucial task for applications like traffic management and surveillance. Current research heavily focuses on improving data association—the process of correctly linking observations of the same object across different cameras and time—often employing transformer-based models and self-supervised learning to automatically learn robust object representations and camera relationships, reducing reliance on manual annotation. These advancements are driving significant improvements in tracking accuracy and efficiency, particularly in challenging real-world scenarios with varying lighting and occlusions, ultimately leading to more robust and scalable intelligent video analysis systems.