Efficient dual-stream neural networks: A modeling approach for inferring wild mammal behavior from video data

Abstract

Monitoring animal behavior is crucial for protecting ecosystems, maintaining ecological balance, and improving animal welfare. By utilizing various monitoring devices, a wealth of behavioral data can be collected, which machine learning techniques can then analyze to identify specific behaviors. Artificial neural networks are particularly important in movement ecology. However, current research on animal behavior recognition faces several limitations. Many existing datasets are limited by homogeneous species categories, simplistic environmental conditions, restricted video perspectives, and a lack of alignment with the complexity of real-world environments. Consequently, there is significant room for improvement in the robustness and generalization of automatic animal behavior recognition models. To address these challenges, this paper introduces a diverse dataset of wild mammal behaviors. This dataset includes a wide variety of typical wild mammal species, providing a foundation for enhancing the generality and robustness of recognition models. The videos in this dataset capture different environmental contexts where wild mammals reside, across various times of day. Based on this dataset, a novel and highly efficient wild mammal behavior recognition model, EDNN, is proposed. The EDNN model integrates both temporal and spatial scales and achieves an average recognition accuracy of 79.17 % for basic locomotion behaviors, with a Top-1 accuracy of 81.37 % and a Top-5 accuracy of 98.04 %. These results demonstrate the feasibility of automating animal behavior recognition using large datasets collected from modern monitoring devices. The EDNN model is highly effective for behavior recognition and can be readily applied across diverse species and scenarios. It efficiently processes various video data and contributes to a deeper understanding of the movement ecology of species that are challenging to observe.