Pedestrian Detection and Tracking for Counting Applications in Crowded Situations

This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 % can be achieved.

1. Introduction and Scope

We present a system to detect and track pedestrians in very crowded situations for the purpose of counting them. Applications range from railway transport security, pedestrian traffic management, detection of overcrowding situations in public buildings to tourist flow estimations.