Object detection based on contour learning and template matching

A method of object detecting based on local contour learning and matching is proposed. Firstly, the representative images are obtained through unsupervised clustering to be as templates. The local contour information of template is extracted and generalized as the template feature, at the same time, codebook dictionary of local contour is built up. Secondly, based on codebook dictionary, using simple sliding-window mechanism and vote algorithm to select initial candidate object windows, the final object windows are got from initial candidate windows based on template feature matching. Experimental results demonstrate that our proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.