Pose classification using support vector machines

In this work a software architecture is presented for the automatic recognition of human arm 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 covariance matrix. Finally, a support vector machine is trained to classify different poses using the eigenvalues array. A detailed description of the system is presented. Experimental results and an outline of the usability of the system as a generic shape classification tool are reported