Body actions automatic detection techniques are usually influenced by imaging angle and imaging environment, these factors lead to detection accuracy reduction, in view of this, a new action detection technique combined monocular vision and depth sensor is proposed. Firstly, the key frames corresponding to body actions are filtered, this process helps to fix the problem of action speed difference; then, the convolutional neural network features and magnitude histogram are combined as action descriptors; finally, adversarial networks are adopted to realize domain adaption transfer learning for body action, in order to solve the problem of imaging anger difference of monocular vision. Validation experiments are carried on a public action detection dataset, and the results of which show that the proposed technique improves action detection performance, and the technique overcomes the detection performance reduction caused by monocular vision imaging anger difference, effectively.