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Many human-centered image and video management systems depend on robust human detection. To extract robust features for human detection, this paper investigates the following shortcomings of co-occurrence histograms of oriented gradients (CoHOGs) which significantly limit its advantages: (1) The magnitudes of the gradients are discarded, and only the orientations are used; (2) the gradients are not smoothed, and thus, aliasing effect exists; and (3) the dimensionality of the CoHOG feature vector is very large (e.g., 200 000). To deal with these problems, in this paper, we propose a framework that performs the following: (1) utilizes a novel gradient decomposition and combination strategy to make full use of the information of gradients; (2) adopts a two-stage gradient smoothing scheme to perform efficient gradient interpolation; and (3) employs incremental principal component analysis to reduce the large dimensionality of the CoHOG features. Experimental results on the two different human databases demonstrate the effectiveness of the proposed method.