Motion Tracking

Motion tracking aims to accurately capture and represent the movement of objects or body parts, primarily using image data from various sources like ultrasound, MRI, and video. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks, generative adversarial networks, and implicit neural representations to improve accuracy and efficiency, particularly in challenging scenarios like noisy or sparse data. These advancements have significant implications for medical imaging (e.g., improved cardiac function analysis, surgical navigation) and robotics (e.g., enhanced robot control and interaction), offering more precise and efficient data analysis and automation.