Abstract: In the multi-target detection and tracking method, lidar (2D laser scanner) scans point cloud data of surroundings and transfers the collected data to the edge server. Then, the edge server uploads the data to the cloud. After obtaining the lidar data, point clouds of footsteps are extracted through dynamic point extraction, point clustering, and random forest model, respectively. Footsteps are matched to form human tracking trajectory by using trajectory matching. After the tracking process, the walking information is published to the users, in a visual form. Meanwhile, the gait parameters are saved into files, including walking speed and step length, when human is detected. Comparing to the visual sensor based human tracking methods, the present invention employs lidar to avoid the interference of ambient light, which leads to easier implementation and larger universality, especially for multi-target scenarios.

Abstract: In the multi-target detection and tracking method, lidar (2D laser scanner) scans point cloud data of surroundings and transfers the collected data to the edge server. Then, the edge server uploads the data to the cloud. After obtaining the lidar data, point clouds of footsteps are extracted through dynamic point extraction, point clustering, and random forest model, respectively. Footsteps are matched to form human tracking trajectory by using trajectory matching. After the tracking process, the walking information is published to the users, in a visual form. Meanwhile, the gait parameters are saved into files, including walking speed and step length, when human is detected. Comparing to the visual sensor based human tracking methods, the present invention employs lidar to avoid the interference of ambient light, which leads to easier implementation and larger universality, especially for multi-target scenarios.