Abstract
Head Pose Estimation has always been an essential part for many applications such as autonomous driving and driv-ing assist systems and hence performance optimization provides better performance as well as lower computing and power needs that allows us to run such applications over embedded devices inside these systems. In this article we present an implementation over a Single board computer for a new system of 3D Head pose estimation that estimates the Head pose of a person in real-time for applications such as Driver monitoring systems, Drones, Gesture recognition and tracking devices. The system is developed over a single board computer (SBC) that is suitable for very low powered applications, it only utilizes the data provided through the IR camera sensor to estimate both the Head and camera pose without any need for external sensors. This system will combine methods that include traditional image processing techniques for image projection, feature detection, key point description and 3D pose estimation along with Machine Learning techniques for face detection and facial landmarks detection.
Highlights
Realtime head pose estimation is a critical problem for many applications in the current industry
Face landmarks detection is the last step before Head pose estimation to detect the preselected points in Human’s face, it starts by providing the algorithm with location of the face within the image frame bounded by a box as in Fig. 2, the model processes this bounding box to provide the location of some points in the face as in Fig. 3 where we can identify the eye, eyebrows, nose, mouth and jawline if needed
This step is done for a single camera sensor at the first setup and these values later on to be used as standard configuration for the system, they need to be done for autofocus sensors and provide some kind of look up table or through equation as these parameters are dependent on the focal point
Summary
Realtime head pose estimation is a critical problem for many applications in the current industry. This technique can be used to provide real-time face recognition for tracking and monitoring systems on low cost boards, it can support future work in gesture and facial impressions recognition In this approach we target the detection of head pose to support many applications like Driver monitoring systems, tracking drones, Gesture controls and augmented reality. It incorporates the usage of a 3D model to map the 2D detected points on the image plan over a 3D plan so that we can get the U, V & W 3D world coordinates Using this method to find the extrinsic parameters in real-time for human’s head R and T matrices to estimate the pose.
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