Human detection in compressed domain

We propose an algorithm for human detection in JPEG compressed still images and MPEG I-frames. In this new algorithm, the overall shape of a standing or walking person is detected by using an eigenspace representation of human silhouettes obtained from AC-DCT coefficients. Our approach is invariant to changes in intensity, color and textures and has the advantage of using the available data in the standard compression algorithms. The algorithm achieves a correct detection rate of 80% for frontal and rear views of human bodies in cluttered scenes.