Active participant identification and tracking using depth sensing technology for video conferencing

Video conferencing represents an effective method of point-to-point or multipoint real-time communication between two or more participants. However, persistent manual adjustments of the video capture device to focus on an active participant represent a challenge, especially if the conference participant moves out of the video capture window. As such, this paper proposes an active-based participant identification and tracking system, which continuously tracks and automatically adjusts the video capture device to maintain focus of the active conference participant. The proposed system first applies a haarcascade face detection algorithm to register and store a set of facial images of the active participant. By leveraging on the depth sensing technology of Microsoft Kinect, this system compares the captured skeletal head position images of participants within the Kinect camera viewpoint, which is then compared against the aforementioned stored face detection images using the principle component analysis face recognition algorithm. The recognized user by the system is then continuously tracked as a skeletal object via a custom designed vertical and horizontal servo controlled motorized system. The custom motorized system sits under the Kinect sensor and is able to achieve 180 degrees in horizontal panning and 22.7 degrees in vertical tilting in line with tracking the movement of the active conference participant.