Pose classification using support vector machines

The field of human-computer interaction has been widely investigated in the last years, resulting in a variety of systems used in different application fields like virtual reality simulation environments, software user interfaces, and digital library systems. A very crucial part of all these systems is the input module which is devoted to recognize the human operator in terms of tracking and/or recognition of human face, arms position, hand gestures, and so on. In this work a software architecture is presented, for the automatic recognition of human arms poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues vector of the pixels co-variance matrix. This kind of information is strictly related to the shape of the contour of the operator figure, and can be usefully employed in order to assess the arms' position. Finally, a support vector machine (SVM) is trained in order to classify different poses, using the eigenvalues array A detailed description of the system is presented along with some remarks on the statistical analysis we used, and on SVM. The experimental results, and an outline of the usability of the system as a generic shape classification tool are also reported.