Multiple Object Tracking

Multiple object tracking (MOT) aims to identify and track multiple objects across video frames, assigning unique IDs to each object and maintaining their trajectories over time. Current research focuses on improving robustness to challenges like occlusions, complex motion patterns (e.g., in sports or dance), and variations in object appearance, often employing deep learning models such as transformers and graph neural networks, along with innovative techniques like self-supervised learning and continual learning. These advancements are crucial for applications ranging from autonomous driving and robotics to video surveillance and sports analysis, driving significant progress in both algorithm design and benchmark dataset development.