Keypoints in image annotation mark precise landmarks—such as eyes, joints, corners, or facial contours—on objects to capture their structure, pose, geometry, and dynamics for training advanced computer vision models. Unlike bounding boxes that enclose entire objects or segmentation that labels every pixel, keypoints provide sparse, coordinate-based data (x,y positions) that explicitly model shape, orientation, deformation, and motion, enabling richer relational understanding.
Annotation Process
Annotators place dots at predefined keypoints using specialized tools, often connecting them with lines to form skeletons (e.g., 17-point COCO human pose model linking shoulders-elbows-wrists-hips-knees-ankles). For facial tasks, 68-point models (like dlib) target eye corners, nose tip, lip edges, and jawline; multi-view or video datasets add temporal consistency across frames to track movement.
Practical Examples
In human pose estimation, keypoints on a runner’s limbs allow models to detect gait abnormalities or sports form; facial keypoints enable emotion recognition (raised eyebrows for surprise) or AR filters aligning virtual glasses. Industrial uses include robotic grasping (keypoints on tool handles) or vehicle pose (wheel positions for alignment).
Applications and Challenges
Key applications span human pose/gesture estimation, facial recognition, action detection, AR/VR body tracking, driver monitoring, and pedestrian behavior analysis in surveillance. Precision is paramount—sub-pixel accuracy via zoom tools and guidelines is essential, as small offsets propagate errors in pose regression or heatmap-based models like Open-pose; inter-annotator agreement protocols and AI pre-labeling mitigate fatigue in large datasets.
