Tracking a variable number of pedestrians in crowded scenes by using laser range scanners

We propose a novel system for tracking a variable number of pedestrians in crowded scenes by exploiting laser range scanners. Based on the specific pattern generated by walking feet in the spatio-temporal domain, a walking model is constructed and applied to track pedestrians. To track interactive targets, an algorithm based on Interactive Multiple Particle Filters (IMPF) is proposed, whose computation load increases linearly with the number of targets. To handle a variable number of pedestrians, spatio-temporal correlation analysis in combination with a mean shift based clustering technique is proposed. Compared with camera-based surveillance methods, our system provides a novel technique for automatically tracking a large number of pedestrians in a relatively large area. The experiments, in which over 2600 pedestrians were tracked in 10 minutes at a 60 m × 20 m subway station, show the effectiveness of our proposed algorithm.