The invention discloses a
human behavior recognition method integrating the space-time dual-network flow and an attention mechanism. The method includes the steps of extracting moving
optical flow features and generating an
optical flow feature image; constructing independent time flow and spatial flow networks to generate two segments of high-level
semantic feature sequences with a significant
structural property; decoding the high-level
semantic feature sequence of the time flow, outputting a time flow visual
feature descriptor, outputting an attention saliency feature sequence, and meanwhile outputting a spatial flow visual
feature descriptor and the
label probability distribution of each frame of a video window; calculating an attention confidence scoring coefficient per
frame time dimension, weighting the
label probability distribution of each frame of the video window of the spatial flow, and selecting a
key frame of the video window; and using a softmax classifier decision to recognize the
human behavior action category of the video window. Compared with the prior art, the method of the invention can effectively focus on the
key frame of the appearance image in the originalvideo, and at the same time, can select and obtain the spatial saliency region features of the
key frame with high recognition accuracy.