Head-pose estimation is a process that involves determining the orientation and position of a person's head in space. Real-time human head pose estimation with ONNXRuntime and OpenCV involves using deep learning models to detect and track the head in real-time, and then using these detections to estimate the head pose.

ONNXRuntime is a framework for running ONNX models on any runtime platform. It provides a high-level API that abstracts away the details of how the model is compiled and executed. This makes it easy to use ONNXRuntime to run models that are optimized for different hardware and platforms.

OpenCV is a computer vision library that provides a range of functions for image processing and computer vision tasks. It includes tools for detecting and tracking objects, including faces and heads.

To perform head-pose estimation with ONNXRuntime and OpenCV, you would first need to train a deep learning model that can detect and track the head in real-time. Once you have trained your model, you can use it to predict the pose of the head in a new image. You can then use OpenCV to apply filters and transformations to the detected head region to improve the accuracy of your pose estimation.Realtime human head pose estimation with ONNXRuntime and OpenCV.