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

Abstract

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.