In view of the behavior recognition technology of mobile vision devices in security scenarios, this paper first describes the research and application progress of behavior recognition technology in security scenarios, and expounds the difficulties in its actual detection tasks, such as camera movement, behavior occlusion, illumination variation, background interference, multi-view variation, and interclass similarity. Then, according to the different network structures, the architecture composition and recognition characteristics of the model based on the two-stream convolutional architecture, the 3D convolutional architecture and the behavior recognition method based on the self-attention mechanism are detailed analyzed and elaborated. Then, the deployment and application of relevant behavior recognition algorithms are carried out on high-performance GPU and embedded microcomputer platforms. The accuracy, detection rate, parameter quantity and actual detection effect are compared and analyzed. Finally, based on the theoretical analysis and comparative experimental results, the limitations of the current behavior recognition algorithm are summarized, and the development direction of algorithm lightweight and adaptability improvement is pointed out.