Detecting Persons using Hough Circle Transform in Surveillance Video

Robust person detection in real-world images is interesting and important for a variety of applications, such as visual surveillance. We address the task of detecting persons in elevator surveillance scenes in this paper. To get more passengers in the lift car, the camera usually installed at the corner of ceiling. However, the high and space of lift car are limited, which makes person occluded by each other or some parts of body invisible in captured images. In this paper, we propose a novel approach to detect head contours, which includes three main steps: pre-processing, head contour detection and post-processing. Hough circle transform is adopted in the second stage, which is robust to discontinuous boundaries in circle detection. Proposed pre-processing and post-processing methods are efficient to remove false alarms on background or body part. Experimental results show our proposed approach is time saving and has better person detection results than some other methods.