Multi Object Tracking

Multi-object tracking (MOT) aims to identify and continuously track multiple objects within video sequences, a crucial task for applications like autonomous driving and surveillance. Current research emphasizes improving robustness and accuracy, particularly in challenging scenarios involving occlusions, complex motion, and diverse object appearances, often employing tracking-by-detection frameworks enhanced with techniques like deep learning-based feature extraction (e.g., ReID), graph neural networks, and state-space models for motion prediction. These advancements are driving significant improvements in MOT performance across various benchmarks and datasets, leading to more reliable and efficient systems for real-world applications.