Adaptive receptive field U-shaped temporal convolutional network for vulgar action segmentation

Temporally detecting and classifying action segments in untrimmed videos are significant for many applications, especially for the detection of vulgar actions such as sucking and caressing in video platform supervision and surveillance applications. At present, vulgar action segmentation has problems such as fuzzy spatial features and complex temporal features of the video, which affect the detection accuracy. Therefore, this paper proposed an effective Adaptive receptive field U-shaped Temporal Convolutional Network (AU-TCN) for the automatic and accurate detection of vulgar actions in the video. Firstly, considering that the current temporal convolutional network has a significant effect on temporal feature extraction, AU-TCN uses the adaptive receptive field convolution kernel to solve the problem of large differences in the average duration between different types of actions in the Internet videos and then realize the temporal attention mechanism. Secondly, the U-shaped structure based on the temporal convolutional network is introduced to effectively analyze both high-level and low-level temporal features of the model, to solve the problem that the spatial features of vulgar actions are not obvious. Finally, extensive experiments on multiple data sets, including public datasets and a self-built vulgar dataset, verify the effectiveness of the proposed model. Our method achieves state-of-the-art results on the vulgar action dataset. This is of great significance to the purification of the Internet environment.