Probabilistic tracking with optimal scale and orientation selection

Abstract

We describe a probabilistic framework based on a trust-region method to track rigid or non-rigid objects with automatic optimal scale and orientation selection. The approach uses a flexible probability model to represent an object by its salient features such as color or intensity gradient. Depending on the weighting scheme, features will contribute to the distribution differently according to their positions. We adopt a bivariate normal as the weighting function that only features within the induced covariance ellipse are considered. Notice that characterizing an object by a covariance ellipse makes it easier to define its orientation and scale. To perform tracking, a trust-region scheme is carried out for each image frame to detect a distribution similar to the target's accounting for the translation, scale, and orientation factors simultaneously. Unlike other work, the optimization process is executed over a continuous space. Consequently, our method is more robust and accurate as demonstrated in the experimental results.