Statistical approach to classification of flow patterns for motion detection

We present a new approach for egomotion computation and the detection of independent motion in the scene. In contrast to related work we apply statistical methods which are based on the normal optical flow field. We extract features for supervised and unsupervised training from the normal optical flow field in order to train a Gaussian-distribution classifier (GDC) and a Kohonen feature map. Finally, in a test phase the egomotion computation is done by classifying features extracted from the normal optical flow field into the unknown motion direction. For the detection of independent motion, the scene is divided into regions. For each region a decision is made, whether the normal flow in this region is based on the camera motion or an independently moving object. We present results of this approach which show a recognition rate of up to 97% for the egomotion classification and a detection rate of moving objects of up to 87%