View-independent human motion classification using image-based reconstruction

摘要

We introduce in this paper a novel method for employing image-based rendering to extend the range of applicability of human motion and gait recognition systems. Much work has been done in the field of human motion and gait recognition, and many interesting methods for detecting and classifying motion have been developed. However, systems that can robustly recognize human behavior in real-world contexts have yet to be developed. A significant reason for this is that the activities of humans in typical settings are unconstrained in terms of the motion path. People are free to move throughout the area of interest in any direction they like. While there have been many good classification systems developed in this domain, the majority of these systems have used a single camera providing input to a training-based learning method. Methods that rely on a single camera are implicitly view-dependent. In practice, the classification accuracy of these systems often becomes increasingly poor as the angle between the camera and the direction of motion varies away from the training view angle. As a result, these methods have limited real-world applications, since it is often impossible to limit the direction of motion of people so rigidly. We demonstrate the use of image-based rendering to adapt the input to meet the needs of the classifier by automatically constructing the proper view (image), that matches the training view, from a combination of arbitrary views taken from several cameras. We tested the method on 162 sequences of video data of human motions taken indoors and outdoors, and promising results were obtained.