Automatic multiple human detection and tracking for visual surveillance system

Abstract

Object Tracking is an important task in video processing because of its variety of applications in visual surveillance, human activity monitoring and recognition, traffic flow management etc. Multiple object detection and tracking in outdoor environment is a challenging task because of the problems raised by poor lighting conditions, variation in poses of human object, shape, size, clothing, etc. This paper proposes a novel technique for detection and tracking of multiple human objects in a video. A classifier is trained for object detection using Haar-like features from training image set. Human objects are detected with help of this trained detector and are tracked using particle filter. The experimental results show that the proposed technique can detect and track multiple humans in a video adequately fast in the presence of poor lighting conditions, variation in poses of human objects, shape, size, clothing etc. and the technique can handle varying number of human objects in a video at various points of time.