Accuracy analysis of farmer behaviour based on big data and efficient video transmission: A convolutional neural network approach

Abstract

Abstract The behaviour analysis of farmers is very important to gradually improve the sustainable production capacity of the land and promote the economics reasonable increasement. How to effectively extract video features and identify human behaviour is a current research hotspot. In this paper, the authors propose a convolutional neural network model based on the deformable convolution including a residual module and an attention module. The model fuses and encodes video spatial features on the time axis and extracts semantic information. The authors combine the attention mechanism to extract local and global features in the video, which are used to calculate spatial features and micro‐motion features, respectively. Temporal attention mechanism fuses each micro‐motion feature of video to extract higher‐level semantic features of motion. Next, the authors describe their proposed model based on deep learning. First, the authors introduce a video frame sampling method based on the TRN algorithm. Second, they design a feature extraction and fusion model based on the ResNet model and the attention mechanism that extract local and global features in videos. Finally, the authors classify and identify the extracted video features through the classifier. The authors’ approach shows comparable performance on publicly available datasets.

Keywords

• accuracy analysis
• big data
• convolutional neural network
• systems
• video transmission