Procrustean decomposition for orthogonal cascade detection

In this paper, our goal is to speed up a standard sliding window detector while maintaining detection accuracies. We do this by decomposing its weight template into multiple component detectors with no redundancy. Each component detector captures partial discriminative appearance, and they can be used in a cascade detection pipeline to aggressively reduce the size of search space. We formulate the decomposition problem as an orthogonal procrustes problem. We further approximate the component detector in the first cascade layer as separable 2D filters (i.e. a product between two vector filters). The running time of such component detector is thus reduced from quadratic to linear with the dimension lengths of the detector template. We conduct extensive experiments using our approach on two well-known object detection datasets: INRIA pedestrian detection dataset and PASCAL VOC 2007 detection dataset. We used HOG features and CNN features in our experiments. Our approach is almost "free lunch": without manipulating the feature representations, it makes the detection process several times faster.