> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/ai-blocks/pose-estimation.md). # Pose Estimation This function block detects human body keypoints and (optionally) draws a skeleton on incoming images. Use it to extract selected body part positions for analytics, logging, or visualization. ## đŸ“Ĩ Inputs `Image Any` The image to analyze (camera frame, loaded image, or preprocessed image). `Show Skeleton` Boolean input to enable or disable drawing the skeleton on the output image. ## 📤 Outputs `Image Any` The image with skeleton drawn (if `Show Skeleton` is enabled) and visual markers for detected keypoints. `Selected Body Part Positions` A dictionary-like result mapping selected body part names to their detected positions (x, y). Only the body part groups you choose are returned. ## 🕹ī¸ Controls `Class Names` A selectable list of simplified body-part groups (for example: head, chest, elbow, hand, hip, knee, foot). Check the groups you want the block to report. `Confidence` A slider to adjust detection confidence. Increasing value reduces false positives but may miss faint detections; lowering it makes detection more permissive. ## ⚙ī¸ Running mechanism * When an image is provided at `Image Any`, the block analyzes the image and tries to locate human keypoints. * If `Show Skeleton` is TRUE, the block overlays the skeleton lines and markers on the output image. * The block returns the annotated image and a mapping of the selected body part names to their detected positions. If a part is not confidently detected it will be omitted from the mapping. ## đŸŽ¯ Features * Visual skeleton overlay for quick inspection. * Selectable body-part groups to limit outputs to only the parts you need. * Adjustable confidence control to balance sensitivity vs. false detections. * Real-time friendly for live camera streams when paired with the appropriate image input block. ## 📝 Usage instructions 1. Provide an image source into `Image Any` (for example: `Camera USB`, `Load Image`, or `Stream Reader`). 2. Choose which body-part groups to output using `Class Names`. 3. Adjust detection sensitivity using the `Confidence` slider. 4. Optionally send a boolean into `Show Skeleton` to enable/disable skeleton drawing. 5. View the result image with `Show Image` or save/log positions for downstream use. ## 💡 Tips and Tricks * Use `Camera USB` as a live image source when working with real-time scenes. * Resize incoming images with `Image Resize` if the people appear too small — larger person pixels improve keypoint accuracy. * Run a fast detector first (for example `Object Detection`) and then crop persons with `Image ROI` to feed individual person images to this block — this can increase reliability and lower processing cost. * To preview results in your workspace, connect this block to `Show Image`. * Save frames containing detections using `Image Logger` or export coordinates with `Data to JSON` or `CSV Export` for later analysis. * Combine with `Draw Result On Image` or `Draw Detections` to annotate additional status text or bounding boxes together with skeletons. ## 🛠ī¸ Troubleshooting * No keypoints detected: try lowering the `Confidence` value or increase input image size with `Image Resize`. * False or jittery keypoints: increase the `Confidence` slider and improve lighting or sharpness (use `Blur` carefully only to remove noise). * People partially out of frame: use an upstream detection/cropping workflow (for example `Object Detection` → `Image ROI`) so the subject is centered before running this block. * Slow performance: reduce input resolution with `Image Resize` or process cropped person regions instead of full-frame images. If you need to log or visualize results, use the suggested combinations under Tips and Tricks to build a robust pipeline.