Loitering Detection Using Bayesian Appearance Tracker and List of Visitors

Abstract

This paper presents a framework of detecting loitering pedestrians in a video surveillance system. When a pedestrian appears in the field of view of the monitoring camera, he/she is tracked by a Bayesian appearance tracker (BAT). The tracker takes the advantage of Bayesian decision to associate the detected pedestrians according to their color appearances among consecutive frames. The pedestrian’s appearance is modeled as a multivariate normal distribution and recorded in a table called list of visitors (LV). LV also records time stamps when the pedestrian appears as an appearing history. Therefore, even though the pedestrian leaves and returns to the scene, he/she can still be recognized and re-identified as a locally or globally loitering suspect by using different rules. A 10-minute video about three loitering pedestrians is used to test the proposed system. They are successfully detected and recognized from other passing-by pedestrians.