Class-Specific Weighted Dominant Orientation Templates for Object Detection

We present a class-specific weighted Dominant Orientation Template (DOT) for class-specific object detection to exploit fast DOT, although the original DOT is intended for instance-specific object detection. We use automatic selection algorithm to select representative DOTs from training images of an object class and use three types of 2D Haar wavelets to construct weight templates of the object class. To generate class-specific weighted DOTs, we use a modified similarity measure to combine the representative DOTs with weight templates. In experiments, the proposed method achieved object detection that was better or at least comparable to that of existing methods while being very fast for both training and testing.