Leg Detection

Leg detection research focuses on accurately identifying and locating legs in various contexts, primarily for robotics and autonomous driving. Current approaches leverage diverse techniques, including deep learning frameworks (like those employing heterogeneous graph neural networks or random forests) and often incorporate sensor data such as proprioceptive information from robots or 2D laser scans. These advancements improve the robustness and efficiency of leg detection, impacting applications ranging from humanoid robot control and contact estimation to pedestrian detection in self-driving vehicles, where reliable leg detection enhances overall system performance and safety.