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We present an oriented holistic feature, namely weighted oriented pixel change history(WOPCH), to describe reciprocating motions and differentiate actions similar in appearance for human action recognition. To construct the oriented representation, we incorporate motion information into pixel change history(PCH) image, through splitting the PCH image into several oriented channels according to the corresponding motion direction. Moreover, relative velocities of body parts are also considered and therefore the accumulating is adapted with the weight of each pixel's relative speed. Afterwards, invariant features are extracted from WOPCH images and a naive Bayes model is used for recognition. Experimental results show that our method outperforms traditional holistic approaches without requiring lots of training samples.