SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition.

Area of Science:

• Computer Vision
• Machine Learning
• Animal Science

Background:

• Accurate keypoint detection is crucial for animal identification systems.
• Deep learning models often require extensive labeled data for optimal performance.
• Existing methods may struggle with minimal training datasets.

Purpose of the Study:

• To introduce SURABHI, a self-training scheme for enhancing deep-learning keypoint detection.
• To improve keypoint detection accuracy by generating effective, machine-annotated 'hard' instances.
• To boost the performance of cattle identification systems, particularly the Eidetic Cattle Recognition System.

Main Methods:

• Developed SURABHI, a self-training methodology for generating machine-annotated instances.
• Focused on creating 'hard' instances that challenge keypoint detection models.
• Engineered the scheme for predicting cattle keypoints from a top-down view.
• Integrated SURABHI with the Eidetic Cattle Recognition System.

Main Results:

• SURABHI significantly improved keypoint detection accuracy without altering model architecture.
• Achieved a top-6 cow recognition accuracy of 91.89% on a cow video dataset.
• Increased the number of correctly identified cow instances by 22% compared to fully supervised training.
• Demonstrated substantial accuracy gains, especially with minimal available training data.

Conclusions:

• SURABHI is an effective self-training scheme for improving deep-learning keypoint detection.
• The method enhances cattle identification accuracy, proving valuable for systems like Eidetic Cattle Recognition.
• SURABHI offers a robust solution for scenarios with limited training data, outperforming traditional supervised methods.