Approximation of feature pyramids in the DCT domain and its application to pedestrian detection
Feature extraction from each scale of an image pyramid to construct a feature pyramid is considered as a computational bottleneck for many object detectors. In this paper, we present a novel technique for the approximation of feature pyramids in the 2D discrete cosine transform (2DDCT) domain. The proposed method is based on a feature resampling technique in the 2DDCT domain, and exploits the effect of resampling an image on the feature responses. Experimental results show that the proposed scheme provides feature approximation accuracy higher than that of the spatial domain counterpart using gradient magnitude or gradient histograms. Further, when the proposed method is employed for pedestrian detection, it provides a logaverage miss-rate lower than that provided by the state-of-the-art techniques on INRIA, ETH, and TUD datasets and performs favorably on Caltech dataset, while performing in real-time.