Tracking of multiple people in crowds using laser range scanners

Abstract

In everyday life, we can see amazing choreographies of movements of crowds of pedestrians. Pedestrians run into and avoid each other but do not seem to consciously cooperate. In this paper, we track a crowd of pedestrians in a large covered and cluttered area to understand their social behavior. Additionally, we try to analyze the characteristics of crowds of pedestrians such as traffic density, velocity, and trajectory. We introduce a stable feature extraction method based on accumulated distribution of successive laser frames. To isolate pedestrians, we propose a non-parametric method exploiting the Parzen windowing technique. We apply the new method of Rao-Blackwellized Monte Carlo data association to track a highly variable number of pedestrians. The algorithm is quantitatively evaluated through a social behavior experiment taking place in the lobby of a school. During this experiment, nearly 300 students are tracked.