Novel method for the recognition of Jinnan cattle action using bottleneck attention enhanced two-stream neural network

Effective and accurate action recognition is essential to the intelligent breeding of the Jinnan cattle. However, there are still several challenges in the current Jinnan cattle action recognition. Traditional methods are based on manual characteristics and low recognition accuracy. This study is aimed at the efficient and accurate development of Jinnan cattle action recognition methods to overcome existing problems and support intelligent breeding. The acquired data from the previous methods contain a lot of noise, which will cause individual cattle to have excessive behaviors due to unsuitability. Concerning the high labor costs, low efficiency, and low model accuracy of the above approaches, this study developed a bottleneck attention-enhanced two-stream (BATS) Jinnan cattle action recognition method. It primarily comprises a Spatial Stream Subnetwork, a Temporal Stream Subnetwork, and a Bottleneck Attention Module. It can capture the spatial-channel dependencies in RGB and optical flow two branches respectively, so as to extract richer and more robust features. Finally, the decision of the two branches can be fused to gain improved cattle action recognition performance. Compared with the traditional methods, the model proposed in this study has achieved state-of-the-art recognition performance, and the accuracy of motion recognition was 96.53%, which was 4.60% higher than other models. This method significantly improves the efficiency and accuracy of behavior recognition and provides an important research foundation and direction for the development of higher-level behavior analysis models in the future development of smart animal husbandry.