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This paper proposes a method of identified reciprocating motion in pornographic video from other human action using Hidden Markov Model (HMM). The motion vectors are obtained by decoding the compressed MPEG video. Then the feature vectors are extracted by calculating the direction and the magnitude of the motion vectors. The feature vectors are fed to Hidden Markov Model for training and classification of actions. Six actions were trained with distinct HMM for classification. The correct recognition result is up to 90%.