Detecting Suspects by Observing Behavioral Changes of Surrounding Pedestrians

Abstract

The recent attacks in towns have increased demands for technology to find suspects in a crowd. A typical way to find suspects is to use facial recognition systems, however, those require a huge amount of personal data in advance. In this paper we propose a method to detect suspects in a crowd by observing pedestrians' behavior without using any specific personal data. The fundamental idea is that if a pedestrian finds a suspect, they might stop walking, or change the direction. We have devised a method to find such changes of behavior of pedestrians based on a Kalman filter and a hidden Markov model. The filter is used to detect a change, and the HMM is for assuming the intention of each observed pedestrian. Agent simulation results shows that the method works sufficiently well, especially where people are walking not in a single direction but in various different directions.