Object tracking remains a central problem in computer vision with broad applications in surveillance, autonomous driving, augmented reality, and human–computer interaction. This paper presents a comprehensive survey that unifies the progression of tracking methodologies, from handcrafted and probabilistic models to deep learning paradigms and recent advances with large vision–language and foundation models. We categorize tracking into Single Object Tracking (SOT), Multi-Object Tracking (MOT), and Long-Term Tracking (LTT), systematically reviewing CNN, Siamese, transformer, and hybrid-based approaches alongside detection-guided, detection-integrated, and re-identification–aware pipelines. Special emphasis is placed on emerging trends, including open-vocabulary tracking, promptable models, and multimodal fusion across RGB, depth, thermal, LiDAR, and event-based inputs. We highlight benchmark datasets, evaluation protocols, and taxonomy refinements that reveal convergence toward unified and generalizable tracking systems. Finally, we discuss open challenges—such as occlusion, scalability, identity consistency, and cross-dataset transferability—and outline future directions in self-supervised learning, adapter tuning, and efficient foundation model adaptation. This survey aims to serve as a reference for both academic researchers and practitioners, bridging classical paradigms with the rapidly evolving landscape of foundation and vision- language driven tracking.
Raja et al. (Mon,) studied this question.