Tracking Algorithm

Object tracking algorithms aim to automatically identify and follow objects in image sequences, a crucial task with applications ranging from autonomous driving to robotics and surveillance. Current research emphasizes improving robustness in challenging conditions like low light, camouflage, and occlusion, often employing deep learning models such as transformers and convolutional neural networks within both single-stage and two-stage tracking frameworks. These advancements are driving improvements in accuracy and efficiency, particularly for multi-object tracking and applications requiring real-time performance, impacting fields that rely on precise and reliable object localization.