Laser-Based Bidirectional Pedestrian Counting via Height Map Guided Regression and Voting

Abstract

Pedestrian counting is valuable for business decisions and public safety, but it remains a challenging task in dynamic environments with illumination changes, shadows, background clutter, etc. In this paper, we investigate a novel laser-based bidirectional pedestrian counting approach that addresses these problems effectively. In the proposed method, a laser range scanner is employed in a bird’s eye view to measure the distances between the laser range scanner and obstacles below. Height data of passing pedestrians are accumulated from each scan to generate a height map with noise eliminated. In order to count the number of pedestrians walking in close distances, we apply linear regression to estimate the number of pedestrians in a height cluster using several geometrical features. A variable-sized slide window is then utilized to detect heads in height data. Furthermore, the direction of each pedestrian is determined by voting results of scanning points in the detected head. Experimental results in different environments demonstrate the accuracy and efficiency of our method.