🤖 AI Summary
To address the longstanding absence of publicly available, fine-grained video datasets for porcine behavior recognition, this study introduces the first open-source video dataset comprising 13 welfare-critical pig behaviors. We further propose ST-ANet, an attention-guided spatiotemporal awareness and enhancement network featuring a two-stage architecture: Stage I localizes behavior-relevant regions and models individual and interactive dynamics via spatiotemporal graph convolution; Stage II incorporates feature recalibration and long-range temporal modeling to strengthen spatiotemporal dependencies. Evaluated on our curated dataset, ST-ANet achieves a mean Average Precision (mAP) of 75.92%, outperforming the best conventional method by 8.17 percentage points. The framework significantly improves accuracy in individual behavior recognition and demonstrates enhanced generalizability across diverse farming scenarios.
📝 Abstract
The recognition of pig behavior plays a crucial role in smart farming and welfare assurance for pigs. Currently, in the field of pig behavior recognition, the lack of publicly available behavioral datasets not only limits the development of innovative algorithms but also hampers model robustness and algorithm optimization.This paper proposes a dataset containing 13 pig behaviors that significantly impact welfare.Based on this dataset, this paper proposes a spatial-temporal perception and enhancement networks based on the attention mechanism to model the spatiotemporal features of pig behaviors and their associated interaction areas in video data. The network is composed of a spatiotemporal perception network and a spatiotemporal feature enhancement network. The spatiotemporal perception network is responsible for establishing connections between the pigs and the key regions of their behaviors in the video data. The spatiotemporal feature enhancement network further strengthens the important spatial features of individual pigs and captures the long-term dependencies of the spatiotemporal features of individual behaviors by remodeling these connections, thereby enhancing the model's perception of spatiotemporal changes in pig behaviors. Experimental results demonstrate that on the dataset established in this paper, our proposed model achieves a MAP score of 75.92%, which is an 8.17% improvement over the best-performing traditional model. This study not only improces the accuracy and generalizability of individual pig behavior recognition but also provides new technological tools for modern smart farming. The dataset and related code will be made publicly available alongside this paper.