RGB D Tracking

RGB-D tracking aims to improve object tracking accuracy by integrating both color (RGB) and depth (D) information from video sequences. Current research focuses on developing robust and generalizable models, including those employing transformer architectures and multi-modal fusion techniques, that can effectively leverage depth data, even learning depth information from RGB data alone. This field is significant due to its potential to enhance applications such as augmented reality, robotics, and autonomous driving, where accurate and reliable object tracking is crucial. The development of large-scale benchmark datasets is also driving progress by enabling more rigorous evaluation and comparison of different tracking methods.