Markerless tracking and gesture recognition using polar correlation of camera optical flow

We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm that captures five degrees of freedom including direction of translation and angular velocity. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80 % over 185 frames, and it is able to track large range motions even in outdoor settings. We also present how our tracking system can be used for gesture recognition by combining it with a simple linear classifier over a set of 15 gestures. Experimental results show that we are able to achieve 86.7 % gesture recognition accuracy.