> 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/social-distance-detector.md). # Social Distance Detector This function block analyzes an image stream to detect people and check physical distancing based on a distance threshold. It visualizes detected people and highlights pairs that violate the specified distance. ## đŸ“Ĩ Inputs `Image Any` Feed the image or video frame to be analyzed. `Perspective Matrix` Optional transformation matrix to convert image coordinates to a real-world plane for more accurate distance measurement. `Distance Threshold` The minimum allowed distance between people (units depend on perspective calibration). ## 📤 Outputs `Image Any` Annotated image with detected people, connecting lines, and violation highlights. `Person Count` Number of people detected in the frame. `Violation Count` Number of pairwise violations detected (pairs closer than threshold). `Is Violated ?` Boolean indicating whether any violation exists. ## 🕹ī¸ Controls `Confidence Ratio` Slider that adjusts detection confidence sensitivity. Higher values require stronger detection confidence to count as a person. ## 🎨 Features * Real-time person detection and visualization on incoming images. * Pairwise distance measurement between detected people. * Optional perspective correction using a provided `Perspective Matrix` for more accurate real-world distance checks. * Clear outputs for monitoring and downstream processing: image, counts, and violation flag. ## 📊 Running mechanism When active, this block receives the image input, detects person positions in the frame, optionally maps those positions using the provided `Perspective Matrix`, computes pairwise distances, and compares them to the provided `Distance Threshold`. The block returns an annotated image and numeric/boolean outputs describing detection and violation state. ## 📝 How to use 1. Provide an image source to `Image Any` (camera stream or loaded image). 2. If you need real-world distances, supply a calibrated `Perspective Matrix`. Without it, distance checks use image-plane units. 3. Set the desired `Distance Threshold` according to your calibration or approximate pixel distance. 4. Tune `Confidence Ratio` to balance missed detections vs false positives. 5. Read the outputs to trigger alerts, logs, or further processing when violations occur. ## 💡 Tips and Tricks * For camera input, pair this block with an image source such as `Camera USB`, `Camera IP (ONVIF)`, `Stream Reader`, or `Load Image`. * To preview results in a larger view, add `Show Image` after this block. * If detection is noisy, try resizing or denoising the image beforehand using `Image Resizer` or `Denoising` to improve stability. * For more robust person detection or custom classes, consider combining with `Object Detection` or `Object Detection - Custom` upstream and feed detected centers into this block for distance checking. * Use `Perspective Transform` to produce a reliable `Perspective Matrix` when you need real-world distances. * Use `Image ROI` or `Image ROI Select` to limit the analysis area (reduce false detections and speed up processing). * When tracking is required across frames, use `Object_Detection_Tracker` downstream to get persistent IDs and improved analytics. ## 🛠ī¸ Troubleshooting * No people detected: increase `Confidence Ratio` slightly or provide higher-resolution input. Ensure the scene lighting allows for clear person outlines. * Many false positives: try increasing `Confidence Ratio`, apply preprocessing like `Blur` or `Image Threshold`, or restrict the area with `Image ROI`. * Distance measurements seem incorrect: verify the calibration and provide a correct `Perspective Matrix` using `Perspective Transform`. Without perspective correction, distances are in image pixels and may not reflect real-world values. * High CPU/GPU load: reduce input resolution with `Image Resizer` or run detection less frequently.