Space-time significance region detection-based human body behavior analysis method

The invention discloses a space-time significance region detection-based human body behavior analysis method. According to the invention, a data set is adopted to rain a Faster R-CNN model. A multi-channel video is input and a single-channel video is segmented into video image frames. The segmented video image frames are subjected to target detection by adopting the Faster R-CNN model. A target detection result is analyzed and a target candidate box is re-calculated. The single-channel video is subjected to box matching for constructing a motion vector field. Based on the motion vector field,the motion vector of the region of interest is calculated. A foreground remarkable motion region is selected based on a probability calculated by utilizing a Gaussian mixture model. According to the target candidate box and the remarkable motion region, a space-time significance region is synthesized. The target space-time significance region is subjected to feature sampling and feature pretreatment. The target space-time