Visual object tracking via Gabor-based salient features extraction

A novel appearance-based method for visual object tracking of rigid objects with pose variations and small scale and 2-dimensional rotation changes is proposed. The algorithm employs a bank of Gabor filters for computing the salient object features, which represent the object model. In each frame, candidate objects of a search region are extracted randomly, following a 2-dimensional Gaussian distribution. The object in the current frame is the candidate object whose cosine similarity to the detected object in the first frame and the object instance in a previous frame where significant change in the object appearance was last observed is maximal.