Visual object tracking by an evolutionary self-organizing neural network

In this paper, a recently proposed evolutionary self-organizing map is extended and applied to visual tracking of objects in video sequences. The proposed approach uses a simple geometric template to track an object executing a smooth movement represented by affine transformations. The template is selected manually in the first frame and consists of a small number of keypoints and the neighborhood relations among them. The coordinates of the keypoints are used as the coordinates of the nodes of a non-regular grid defining a self-organizing map that represents the object. The weight vectors of each node in the output grid are updated by an evolutionary algorithm and used to locate the object frame by frame. Qualitative and quantitative evaluations indicate that the proposed approach present better results than those obtained by a direct method approach. Additionally, the proposed approach is evaluated under situations of partial occlusion and self-occlusion, and outliers, also presenting good results.