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Related Experiment Video

Updated: Jan 28, 2026

Transition of Farm Pigs to Research Pigs using a Designated Checklist followed by Initiation of Clicker Training - a Refinement Initiative

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Published on: August 21, 2021

Automatic Individual Pig Detection and Tracking in Pig Farms.

Lei Zhang¹, Helen Gray², Xujiong Ye³

¹Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK. lzhang@lincoln.ac.uk.

Sensors (Basel, Switzerland)

|March 13, 2019

Summary

This study introduces a new method for tracking individual pigs in videos, even in difficult farm conditions. The system accurately identifies and follows multiple pigs without needing special tags, improving farm management.

Keywords:

computer vision multiple objects tracking object detection surveillance system

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Area of Science:

Computer Vision
Animal Science
Machine Learning

Background:

Individual pig detection and tracking are crucial for video-based farm monitoring.
Challenges include light variations, similar pig appearances, shape changes, and occlusions.

Purpose of the Study:

To develop a robust, on-line method for detecting and tracking multiple pigs without manual identification.
To ensure the method works under both daylight and infrared (nighttime) conditions.

Main Methods:

Coupled a Convolutional Neural Network (CNN)-based detector with a correlation filter-based tracker.
Utilized a novel hierarchical data association algorithm for robust tracking.
Employed a one-stage prediction network with multi-scale features for optimal accuracy/speed trade-off.
Defined a 'tag-box' for each pig and used key-point tracking with learned correlation filters.

Main Results:

The method robustly detects and tracks multiple pigs in complex farm environments.
Successfully addressed challenges like light fluctuation, similar appearances, shape deformations, and occlusions.
Demonstrated effective correction of drifted tracks and integration of tracking fragments.

Conclusions:

The proposed method offers a feasible solution for long-term individual pig tracking in complex settings.
Shows significant commercial potential for advanced livestock monitoring systems.