A video behavior recognition method based on multi-task supervised learning comprises the steps that 1) dividing an input video into a plurality of video sequences, wherein each video sequence comprises a plurality of frames of images; and 2) designing a 3D convolutional neural network to extract time and space joint features of the video sequence, and realizing salient region detection and videobehavior classification, wherein the two tasks share the feature extraction module, and the saliency feature map is used for guiding extraction of behavior recognition features, so that the neural network more pays attention to a behavior occurrence region, the network can learn features sensitive to motion classification, and the detection precision of behavior recognition is improved. Meanwhile,compared with a single-task detection model, the salient region detection accelerates the training speed of the video behavior classification task. On a currently published test data set, the methodprovided by the invention obtains a leading detection level.
Video behavior recognition method based on multi-task supervised learning