  This project investigates the human multi-modal behavior identification
algorithm utilizing deep neural networks. According to the characteristics of
different modal information, different deep neural networks are used to adapt
to different modal video information. Through the integration of various deep
neural networks, the algorithm successfully identifies behaviors across
multiple modalities. In this project, multiple cameras developed by Microsoft
Kinect were used to collect corresponding bone point data based on acquiring
conventional images. In this way, the motion features in the image can be
extracted. Ultimately, the behavioral characteristics discerned through both
approaches are synthesized to facilitate the precise identification and
categorization of behaviors. The performance of the suggested algorithm was
evaluated using the MSR3D data set. The findings from these experiments
indicate that the accuracy in recognizing behaviors remains consistently high,
suggesting that the algorithm is reliable in various scenarios. Additionally,
the tests demonstrate that the algorithm substantially enhances the accuracy of
detecting pedestrian behaviors in video footage.
