Face obscuration in a video sequence by integrating kernel-based mean-shift and active contour

Abstract

A technology for protecting privacy in video surveillance is presented in this paper. Human identity that can contain privacy intrusive information is protected. By integrating mean-shift and active contour, faces can be tracked and blurred in each frame of a video sequence. In the initial frame, faces are located by a face detector. We extend the Adaboost multiview face detector to detect the low-resolution faces. In order to improve the efficiency of the detection and tracking, the background subtraction is used to constrain the face search region. The face is modeled as an ellipse and the centre of the ellipse is predicted using mean shift. The position and scale of the mean shift are updated using the active contour. The combined mean shift and active contour improves the robustness of the tracking. Experimental results show that the algorithm is robust to occlusion and scale variation.