A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

Abstract

A reliable algorithm for head movements inside a vehicle is designed. The proposed algorithm allowed the adjustment of basic functions such as indicators, mirrors and reverse lights based on the driver final head position. The algorithm system mapped a predefined coordinates for driver’s head that resulted in a computable geometry via a sensory system which is fed to the vehicle actuating system. Problem statement: Head position recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. In the past few years face detection and person identification became important issues due to security concerns, leading to head gesture algorithm development and implementation. Approach: This study introduces a new approach that combines Fixed Center Interpolation Net Algorithm (FCIN) with Wight Elimination Algorithm (WEA). This enhances the ability to classify and predict head positions and poses and gives better representation capabilities for the overall system algorithm. Such algorithm is able to handle pattern recognition problems using Radial Basis Function (RBF) models. The system algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. Results: A reliable, fast and robust approach for driver head position recognition is achieved and presented. Conclusion: A simple hybrid algorithm for driver’s head movements is designed and tested. The obtained results proved the algorithm applicability and ability to predict and act upon head gestures.