Online Multi Object Tracking

Online multi-object tracking (MOT) aims to identify and track multiple objects across video frames in real-time, a crucial task in various applications like autonomous driving and sports analytics. Current research emphasizes improving robustness to occlusions and ID switches, often employing deep learning architectures like transformers and incorporating features such as appearance, motion, and even weak cues like object height and confidence. These advancements leverage techniques like contrastive learning for improved object representation and efficient algorithms to maintain real-time performance, leading to more accurate and reliable tracking in challenging scenarios. The resulting improvements have significant implications for fields requiring precise object tracking, enabling more sophisticated applications in areas such as robotics, surveillance, and video understanding.