Abstract
Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is conducive to reducing training costs, realizing efficient calculation, and enabling researchers to quickly apply it to practical scenarios.
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