Multi Modal Tracking

Multi-modal tracking aims to improve the accuracy and robustness of object tracking by integrating information from multiple sensory modalities, such as visual (RGB, thermal, depth, event streams), and audio data, overcoming limitations of single-modality approaches. Current research focuses on effective fusion strategies, often employing transformer-based architectures and techniques like prompt learning and knowledge distillation to leverage pre-trained models and address data scarcity issues. This field is significant for advancing applications in autonomous driving, robotics, and human-computer interaction, where reliable and robust object tracking in challenging environments is crucial.