Binocular Stereovision-Based Dynamic 3D Ear Localization and Tracking for In-Vehicle Acoustic Applications

This paper presents a binocular stereovision method for reconstructing the three-dimensional spatial coordinates of the occupant’s ear, as a laboratory proof-of-concept for future in-vehicle acoustic applications. The binocular system is calibrated using a two-step radial alignment constraint. For robust detection, a coarse-to-fine cascade fuses skin-color, geometric, and contour features, where the skin-color model updates online with a forgetting factor to adapt to illumination while contour matching outputs a confidence score. Scale-invariant feature transform points are extracted and ranked by importance, and stereo-matching is guided by a Kalman-predicted dynamic search window. Match verification is achieved via nearest-neighbor ratio, epipolar geometric consistency, and disparity continuity. An adaptive Kalman filter smooths target positions on the two-dimensional image planes, after which smoothed coordinates are triangulated into three-dimensional spatial coordinates. Both process and observation noise covariances are adaptively adjusted according to estimated acceleration and detection confidence, and the Mahalanobis distance triggers a full-image re-detection upon tracking loss. Experimental results for a seated participant under simplified in-vehicle conditions show tracking accuracy within five millimeters and a progressive stereo-matching time of about forty-five milliseconds. These results support the feasibility of the proposed framework for future in-vehicle acoustic applications.