Head detection using motion features and multi level pyramid architecture

Abstract

Abstract Monitoring large crowds using video cameras is a challenging task. Detecting humans in video is becoming essential for monitoring crowd behavior. However, occlusion and low resolution in the region of interest hinders accurate crowd segmentation. In such scenarios, it is likely that only the head is visible, and often very small. Most existing people-detection systems rely on low-level visual appearance features such as the Histogram of Oriented Gradients (HOG), and these are unsuitable for detecting human heads at low resolutions. In this paper, a novel head detector is presented using motion histogram features. The shape and the motion information, including crowd direction and ma..