The invention discloses a 
human motion identification method based on second generation Bandelet statistical characteristics, which mainly solves the problems of complicated characteristic extraction and poor representational capacity in the prior art. The 
human motion identification method comprises the steps of: 1, converting video in a Weizmann 
database into a sequence image, constructing a training sample set X and a 
test sample set T according to a proportion of 8:1; 2, carrying out second generation Bandelet transformation on a 
single sequence image in the sample sets, sequentially extracting an energy characteristic Ve, an entropy characteristic Vs, a maximum value characteristic Vmax, a minimum value characteristic Vmin, a 
contrast ratio characteristic Vc, a mean value characteristic Vmu and a variance characteristic Vv of the image, and cascading all the characteristics to be used as final characteristics of the 
single image; and 3, repeating the step 2 for respectively extracting characteristics of all sequence images in the training sample set X and the 
test sample set T to obtain a training sample characteristic set X* and a 
test sample characteristic set T*, and carrying out learning training on the training sample characteristic set X* and the test sample characteristic set T* by using an 
Adaboost algorithm to obtain a classifying result. The 
human motion identification method can be used for accurately identifying a human motion, and can be used in 
video monitoring and 
video processing of target identification.