Head detection for video surveillance based on categorical hair and skin colour models

We propose a new robust head detection algorithm that is capable of handling significantly different conditions in terms of viewpoint, tilt angle, scale and resolution. To this aim, we built a new model for the head based on appearance distributions and shape constraints. We construct a categorical model for hair and skin, separately, and train the models for four categories of hair (brown, red, blond and black) and three categories of skin representing the different illumination conditions (bright, standard and dark). The shape constraint fits an elliptical model to the candidate region and compares its parameters with priors based on human anatomy. The experimental results validate the usability of the proposed algorithm in various video surveillance and multimedia applications.