Motion Based Tracker

Motion-based tracking aims to locate and follow objects over time using their movement patterns, overcoming challenges posed by appearance-based methods in scenarios with occlusion or similar-looking objects. Current research focuses on replacing traditional linear motion models (like Kalman filters) with deep learning approaches, including recurrent neural networks and attention mechanisms, to handle complex, non-linear movements. These advancements are improving the accuracy and robustness of tracking in diverse applications, such as autonomous driving, sports analysis, and bycatch monitoring in fishing, where precise and real-time object localization is crucial. The development of more accurate and efficient motion-based trackers is driving progress in various fields requiring robust object tracking in dynamic environments.