# EML-SlowFast: A behavior recognition model for lion-head goose

## TL;DR

This paper introduces EML-SlowFast, a new model for accurately recognizing the behaviors of lion-head geese, which helps improve their health and productivity in farming.

## Contribution

The novel EML-SlowFast model improves behavior recognition accuracy and reduces computational complexity for lion-head geese.

## Key findings

- EML-SlowFast achieved 92.06% average F1 score for recognizing five lion-head goose behaviors.
- The model reduced computational complexity by 7.358 G FLOPs compared to the SlowFast model.
- EML-SlowFast outperformed existing models in behavior recognition accuracy and efficiency.

## Abstract

The behavior of lion-head goose has a significant impact on their health status, activity levels, and productivity. It is therefore important to monitor the behavior of lion-head geese to enhance their health status, reproductive performance, and overall productivity. However, there is currently no specific behavioral recognition method for lion-head goose, which presents a significant challenge in quickly and effectively identifying various behaviors. To address this issue, this study proposes a model called EML-SlowFast, which is an improvement based on SlowFast. The model is capable of distinguishing five basic behaviors of lion-head goose: feeding, resting, preening, standing, and walking. The Efficient Channel Attention Bottleneck (ECAbneck) module and the Large Kernel Global-Local Feature Extraction (LGLE) module are designed and incorporated into the model. By combining and filtering channel information, the ECAbneck module enhances the model's ability to extract static characteristics from lion-head goose, increasing the accuracy of behavior recognition. The LGLE module captures temporal dependencies in lion-head goose behavior by integrating and extracting local and global features, thereby reinforcing the model's ability to model long-term temporal characteristics and further increasing accuracy. The experiment results showed that the average F1 score, average Precision, Accuracy, and average Recall of the EML-SlowFast model were 92.06 %, 91.60 %, 91.85 %, and 92.78 %, respectively, reflecting improvements of 4.03 %, 3.79 %, 4.14 %, and 4.45 % over the corresponding metrics of the SlowFast model. Furthermore, the FLOPs of the EML-SlowFast model was 10.807 G, which was a reduction of 7.358 G compared to the SlowFast model. Compared to commonly used behavior recognition models, the EML-SlowFast model has effective recognition of lion-head goose behaviors while maintaining low computational complexity, which is beneficial for deployment and use in scenarios with low computational resources. The EML-SlowFast model can rapidly and accurately recognize lion-head goose behaviors, providing a valuable reference for precision farming, reproduction, and health welfare monitoring of lion-head goose.