Vehicle motion analysis based on a monocular vision system

Abstract

This paper presents a comprehensive methodology for on-road vehicle motion analysis using a monocular vision system. Vehicle motion analysis plays an essential role in various intelligent vehicle applications, such as cruise control, vehicle platooning, and collision avoidance. In this paper, it's proposed to improve the accuracy of vehicle motion analysis by breaking the task into two complementary steps: incoming vehicle detection and vehicle motion analysis. In the vehicle detection, a new vehicle which enters the observation field will be identified by inspecting its vehicle-related features. Once a vehicle is detected, a fine-level motion analysis mechanism is employed to monitor its position and relative speed based on the temporal consistency exploitation. Specifically, a novel 3-D Pulse-Coupled Neural Network (PCNN) model is employed for optical flow calculation and optimization. The improved optical flow is then interpreted to generate reliable vehicle motion estimation. Overall, the proposed method shows excellent performance in terms of both accuracy and efficiency owing to its effective coarse-to-fine processing scheme and multiple-cue consideration. (6 pages)