Multi Object Tracking Algorithm

Multi-object tracking (MOT) algorithms aim to automatically identify and track multiple objects across video frames, a crucial task with applications ranging from autonomous driving to animal behavior analysis. Current research emphasizes improving accuracy and robustness, particularly for challenging scenarios involving dense crowds, small or dim objects, and irregular movements, often employing deep learning architectures like transformers and leveraging temporal information through techniques such as particle filtering and Kalman filtering variations. These advancements are driving improvements in tracking performance across diverse datasets and contributing to more reliable and efficient solutions in various fields.