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Computer-Aided Recognition and Analysis of Abnormal Behavior in Video

In intelligent computer-aided video abnormal behavior recognition, pedestrian behavior analysis technology can detect and handle abnormal behaviors in time, which has great practical value in ensuring social safety. We analyze a deep learning video behavior recognition network that has advantages in current research. The network first sparsely sampled the input video to obtain the video frame of each video segment, and then used a two-dimensional convolutional network to extract the characteristics of each video frame, then used a three-dimensional network to fuse them. The method realizes the recognition of long-term and short-term actions in the video at the same time. In order to overcome the shortcoming of the large amount of calculation in the 3D convolution part of the network, this paper proposes an improvement to this module in the network, and proposes a mobile 3D convolution network structure. Aiming at the problem of low utilization of long-term motion features in video sequences, this paper constructs a deep residual module by introducing long and short-term memory networks, residual connection design, etc., to fully and effectively utilize the long-term dynamic features in video sequences. Aiming at the problem of large differences in similar actions and small differences between classes in abnormal behavior videos, this paper proposes a 2CSoftmax function based on double center loss to optimize the network model, which is beneficial to maximize the distance between classes and minimize the distance between classes, so as to realize the classification and recognition of similar actions and improve the recognition accuracy.