A method of object detection based on combination of local and spatial information is proposed.Firstly,the categorygiven representative images are chosen through clustering to be templates,and the local and spatial information of template are extracted and generalized as the template feature.At the same time,the codebook dictionary of local contour is also built up.Secondly,based on the codebook dictionary,sliding-window mechanism and the vote algorithm are used to select initial candidate object windows.Lastly,the final object windows are got from initial candidate windows based on local and spatial structure feature matching.Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods. A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are extracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondarily, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object windows. Lastly, the final object windows are got from the initial candidate windows based on local and spatial structure feature matching. Experimental result demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.