Robust pedestrian detection and tracking in crowded scenes

Abstract

This paper presents a vision based tracking system developed for very crowded situations like underground or railway stations. Our system consists on two main parts - searching of people candidates in single frames, and tracking them frame to frame over the scene. This paper concentrates mostly on the tracking part and describes its core components in detail. These are trajectories predictions using KLT vectors or Kalman filter, adaptive active shape model adjusting and texture matching. We show that combination of presented algorithms leads to robust people tracking even in complex scenes with permanent occlusions.