Evolving a Self-Organizing Feature Map for Visual Object Tracking

An extension of a recently proposed evolutionary selforganizing map is introduced and applied to the tracking of objects in video sequences. In the proposed approach, a geometric template consisting of a small number of keypoints is used to track an object that moves smoothly. The coordinates of the keypoints and their neighborhood relations are associated with the coordinates of the nodes of a self-organizing map that represents the object. Parameters of a local affine transformation associated with each neuron are updated by an evolutionary algorithm and used to map each template’s keypoint in the previous frame to the current one. Computer simulations indicate that the proposed approach presents better results than those obtained by a direct method approach.