Human being daily skill can be exerted fully and bondage can be delivered efficiently in which people use ordinary equipment as an input way if pointing gesture is used for human-computer interaction(HCI).One of key problems is how to reliably recognize pointing user from HCI scene with cluttered background.A novel method has been developed based on spatio-temporal motion.According to multi-scale wavelet transform(MWT)with outstanding local characteristics both in spatial and temporal domains

it is adopted to extract foreground motion subject from cluttered scene.Some disadvantages are overcome including restrictions in environment conditions

dynamic environment variation

and a priori assumption.MWT based gradient integral graph is used to get some HOG feature vectors in pointing hand which are classified and learnt based on machine learning.Pointing user is recognized according to spatial relationship between pointing hand and its corresponding subject.Experimental results have been shown that the proposed method is efficient and viable.