Building a Multiple Object Tracking System with Occlusion Handling in Surveillance Videos

Abstract

Video tracking systems are increasingly used day in and day out in various applications such as surveillance, security, monitoring, and robotic vision. In this chapter, the authors propose a novel multiple objects tracking system in video sequences that deals with occlusion issues. The proposed system is composed of two components: An improved KLT tracker, and a Kalman filter. The improved KLT tracker uses the basic KLT tracker and an appearance model to track objects from one frame to another and deal with partial occlusion. In partial occlusion, the appearance model (e.g., a RGB color histogram) is used to determine an object’s KLT features, and the authors use these features for accurate and robust tracking. In full occlusion, a Kalman filter is used to predict the object’s new location and connect the trajectory parts. The system is evaluated on different videos and compared with a common tracking system.