Adaptive mean-shift tracking with novel color model

Abstract

We describe a new method for robust and real time object tracking using a Gaussian cylindroid color model and an adaptive mean shift. Color information has been used for characterizing an object from others. However, sensitiveness to illumination changes limits their flexibility and applicability under various illuminating conditions. We present an effective color space model against irregular illumination changes where chrominance is fitted with respect to intensity using B spline. A target for tracking is expressed by a joint probabilistic density function that incorporates a proposed color space model into the positional space in image lattice. Tracking is performed using the mean shift algorithm where the bandwidth selection is essential to tracking performance. We present a simple and effective method to find the optimal bandwidth that maximizes the lower bound of the log likelihood of the target represented by the joint probabilistic density function. The robustness and capability of the presented method are demonstrated for several image sequences.