Heterogeneous multi-task smoking behavior recognition model combined with attention.

The traditional behavior recognition model has the disadvantage that it can't get the internal relationship between similar behaviors, such as smoking, pen, chin and the clamped objects, which limits the actual landing of such fine and complex behaviors as smoking recognition. To solve these problems, this paper puts forward the heterogeneous algorithm HMMA-NET (Heterogeneous multi-task smoking behavior recognition model combined with Attention), which consists of two modules: behavior prior and local detection, aiming at establishing the relationship between behavior and behavior objects. CNN combined with channel attention mechanism is used in both behavior prior module and local detection module. The former uses sign language semantic features to complete the primary prior of behavior according to the obtained behavior affinity vector field, while the latter designs network optimization such as fast Edgebox to obtain candidate areas, so as to transfer component information and achieve the goal of fast fine-grained detection. Finally, the two modules use SaaS mode to complete association recognition. Experiment shows that the algorithm can recognize complex actions effectively, and its accuracy is still equal to or even better than that of a single model, in which the accuracy of detecting smoking behavior scenes is 96.10%, and the false detection rate is 3.6%. The algorithm has been commercialized and applied to the actual monitoring of petrochemical scenes. The running results show that the algorithm can maintain good real-time performance and generalization ability. [ABSTRACT FROM AUTHOR]