A self-attention mechanism-based behavior recognition method. The method employs multiangle attention mechanism-based keyframe target position prediction and continuous frame action type prediction modules, thus implementing a target positioning function while achieving continuous frame action detection. In the method, a 3D convolutional network is replaced by the multiangle attention mechanism-based keyframe target position prediction and continuous frame action type prediction modules, the problem of a large computational load of a 3D convolutional network model is solved, the parallel computing power of the model on a GPU is increased, at the same time, the multiangle attention mechanism-based keyframe target position prediction and continuous frame action type prediction modules avoid the problem of weak compatibility during model transformation or deployment due to 3D convolutions being in learning frameworks of different depths.
SELF-ATTENTION MECHANISM-BASED BEHAVIOR RECOGNITION METHOD