Abstract
This paper presents the application of a novel optical flow optimization algorithm for a comprehensive on-road vehicle motion analysis. Optical flow, which contains abundant local motion information, has been extensively studied for vehicle motion estimation in the last decades. How to generate a reliable optical flow at a low computation cost is always a challenging task. The primary aim of this paper is to enhance the accuracy and efficiency of optical flow estimation for a reliable vehicle motion analysis. In the paper, an innovative optical flow optimization algorithm is proposed based on a 3-D Pulse-Coupled Neural Network (PCNN) model. Because of the excellent information clustering ability of PCNN, the proposed algorithm can significantly improve the quality of optical flow. Moreover, a sparse motion flow field is generated to boost the computation efficiency. We employ a preliminary processing to detect the Region of Interest (ROI) in the image, and optical flow is only calculated and optimized in the ROI to save computation resources. Finally, the improved sparse optical flow field is exploited for a systematic on-road vehicle motion analysis. The proposed methodology has been evaluated under various challenging traffic situations to demonstrate its excellent performance.
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