Real time face and object tracking as a component of a perceptual user interface

Abstract

As a step towards a perceptual user interface, an object tracking algorithm is developed and demonstrated tracking human faces. Computer vision algorithms that are intended to form part of a perceptual user interface must be fast and efficient. They must be able to track in real time and yet not absorb a major share of computational resources. An efficient, new algorithm is described here based on the mean shift algorithm. The mean shift algorithm robustly finds the mode (peak) of probability distributions. We first describe histogram based methods of producing object probability distributions. In our case, we want to track the mode of an object's probability distribution within a video scene. Since the probability distribution of the object can change and move dynamically in time, the mean shift algorithm is modified to deal with dynamically changing probability distributions. The modified algorithm is called the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm. CAMSHIFT is then used as an interface for games and graphics.