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Semi-automated annotation for video-based beef cattle behavior recognition.

Zhiyong Cao¹, Chen Li¹, Xiujuan Yang^2,3

¹College of Big Data, Yunnan Agricultural University, Kunming, 650201, China.

Scientific Reports

|May 17, 2025

Summary

This summary is machine-generated.

A new dataset of beef cattle behaviors was created using video analysis. This dataset enables accurate recognition of cattle activities, improving farm efficiency and health monitoring.

Keywords:

Beef cattle behaviour recognition Dataset TimeSformer Video understanding

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Area of Science:

Agricultural Science
Computer Vision
Animal Behavior

Background:

Accurate monitoring of beef cattle behavior is crucial for optimizing animal welfare and farm productivity.
Existing datasets may lack diversity in behaviors, lighting conditions, or annotation methods, limiting their utility.

Purpose of the Study:

To construct a comprehensive video-based dataset for beef cattle behavior recognition.
To develop and evaluate a baseline model for automated cattle behavior analysis.

Main Methods:

Collected 168 hours of video data from six beef cows under various conditions.
Automated data annotation using YOLOv8 and ByteTrack, with manual verification.
Developed a TimeSformer model for multi-behavior recognition and employed data enhancement strategies.

Main Results:

The dataset comprises 500 video clips, 2000 image samples, and over 4000 tracking samples, totaling 14 hours of labeled data.
The TimeSformer baseline model achieved an average recognition accuracy of 90.33% on the test set.
Data enhancement and oversampling strategies effectively addressed data imbalance and overfitting.

Conclusions:

The developed dataset provides a robust foundation for beef cattle behavior recognition research.
Automated behavior recognition holds significant potential for intelligent health monitoring and enhanced farming efficiency.