A unified framework for capturing facial images in video surveillance systems using cooperative camera system

Abstract

Low resolution and un-sharp facial images are always captured from surveillance videos because of long human-camera distance and human movements. Previous works addressed this problem by using an active camera to capture close-up facial images without considering human movements and mechanical delays of the active camera. In this paper, we proposed a unified framework to capture facial images in video surveillance systems by using one static and active camera in a cooperative manner. Human faces are first located by a skin-color based real-time face detection algorithm. A stereo camera model is also employed to approximate human face location and his/her velocity with respect to the active camera. Given the mechanical delays of the active camera, the position of a target face with a given delay can be estimated using a Human-Camera Synchronization Model. By controlling the active camera with corresponding amount of pan, tilt, and zoom, a clear close-up facial image of a moving human can be captured then. We built the proposed system in an 8.4-meter indoor corridor. Results show that the proposed stereo camera configuration can locate faces with average error of 3%. In addition, it is capable of capturing facial images of a walking human clearly in first instance in 90% of the test cases.