Cow Pose Estimation Dataset

Keypoint Annotations:

The dataset includes the following 12 keypoints, strategically marked to represent significant anatomical features of cows:

1. Nose: Essential for head orientation and overall movement tracking.
2. Right Eye: Helps in head pose estimation.
3. Left Eye: Complements the right eye for accurate head direction.
4. Neck (side): Marks the side of the neck, key for understanding head and body coordination.
5. Left Front Hoof: Tracks the front left leg movement.
6. Right Front Hoof: Tracks the front right leg movement.
7. Left Back Hoof: Important for understanding rear leg motion.
8. Right Back Hoof: Completes the leg movement tracking for both sides.
9. Backbone (side): Vital for posture and overall body orientation analysis.
10. Tail Root: Used for tracking tail movements and posture shifts.
11. Backpose Center (near tail’s midpoint): Marks the midpoint of the back, crucial for body stability and movement analysis.
12. Stomach (center of side pose): Helps in identifying body alignment and weight distribution.

Dataset Format:

The data is structure in the COCO format, with annotations that include image coordinates for each keypoint. This format is highly suitable for integration into popular deep learning frameworks. Additionally, the dataset includes metadata like bounding boxes, image sizes, and segmentation masks to provide detail context for each cow in an image.

Compatibility:

This dataset is optimize for use with cutting-edge pose estimation models such as YOLOv8 and other keypoint detection models like DeepLabCut and HRNet, enabling efficient training and inference for cow pose tracking. It can be seamlessly integrate into existing machine learning pipelines for both real-time and post-processed analysis.