State of the Art Tracklets

State-of-the-art tracklets in multiple object tracking (MOT) research focus on robustly associating detected objects across video frames to form accurate and continuous trajectories, even in challenging scenarios like occlusions and dense crowds. Current approaches leverage various techniques, including graph neural networks, transformer architectures, and optimization methods like minimum cost flow, to model both appearance and spatio-temporal relationships between objects within and across tracklets. These advancements improve the accuracy and efficiency of MOT, impacting applications such as autonomous driving, video surveillance, and human-computer interaction by enabling more reliable and detailed understanding of dynamic scenes. The field is actively exploring methods to handle uncertainty in detections, improve long-range dependency modeling, and efficiently scale to longer sequences and multiple cameras.