Human Tracking and Orientation Estimation

Abstract

Human body orientation estimation is useful for analyzing the activities of a single person or a group of people. Estimating body orientation can be subdivided in two tasks: human tracking and orientation estimation. In this paper, the second task of orientation estimation is accomplished by using HoG descriptors and other cues such as the velocity direction, the presence of face, and temporal smoothness. Three different classifiers: Gaussian Mixture Model, Neural Network and Support Vector Machine, are combined with the information from those cues to form a committee. The performance of the method is evaluated and the contribution to the final prediction of each classifier is assessed. Overall, the performance of the proposed approach outperforms the state-of-the-art method, both in terms of estimation accuracy, as well as computation time.