Global-local articulation pattern-based pedestrian detection using 3D Lidar data

Highly variable human poses and pedestrian occlusion make light detection and ranging (Lidar)-based pedestrian detection challenging. This letter proposes a novel framework to address these issues. Other than dividing humans into arbitrary number of parts and using the same features for all part detectors, we represent humans with global-local articulated parts and formulate new features relying on each part's own character. Articulated parts are effective because they each usually maintain a relatively consistent shape across a broader range of body poses. In addition, to extract visible human segments from cluttered surroundings with the presence of pedestrian occlusion, both 3D information and 2D spatial information are used in a coarse-to-fine manner, making the interaction of human part and its neighbouring objects better analysed. The algorithm is evaluated over a busy street dataset and is shown to be competitive with the state-of-the-art Lidar-based algorithms. Remarkably, even at long distances, up to 20 m, it can handle pedestrian occlusion efficiently and effectively.