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In this paper, we present a novel descriptor to characterize human action when it is being observed from a far field of view. Visual cues are usually sparse and vague under this scenario. An action sequence is divided into overlapped spatial-temporal volumes to make reliable and comprehensive use of the observed features. Within each volume, we represent successive poses by time series of Histogram of Oriented Gradients (HOG) and movements by time series of Histogram of Oriented Optical Flow (HOOF). Supervised Principle Component Analysis (SPCA) is applied to seek a subset of discriminantly informative principle components (PCs) to reduce the dimension of histogram vectors without loss of accuracy. The final action descriptor is formed by concatenating sequences of SPCA projected HOG and HOOF features. A Support Vector Machines (SVM) classifier is trained to perform action classification. We evaluated our algorithm by testing it on one normal resolution and two low-resolution datasets, and compared our results with those of other reported methods. By using less than 1/5 the dimension a full-length descriptor, our method is able to achieve perfect accuracy on two of the datasets, and perform comparably to other methods on the third dataset.