Identification and tracking using laser and vision of people maneuvering in crowded environments

This paper presents a people tracker that uses a two-layered laser range sensor (LRS) and a fisheye camera. The LRS detects waists and knees of people, and the camera captures color images of torsos of people detected by the LRS. From the data of people´s position obtained by the LRS and the color histogram obtained by the camera, heuristic-rule-based and global-nearest-neighbor-based data association can identify multiple people in crowded environments. The identified people are tracked via a model-based tracker; an interacting multiple model estimator is applied to track people maneuvers, such as walking, running, sudden starting/stopping, and sudden turning. Simulation and experimental results show that our people identification and tracking method provides better performance than conventional methods.