Vehicle detection framework for challenging lighting driving environment based on feature fusion method using adaptive neuro-fuzzy inference system. (20th April 2018)

• Record Type: Journal Article
• Authors: Pae, Dong Sung; Choi, In Hwan; Kang, Tae Koo; Lim, Myo Taeg
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 2(2018:Mar./Apr.)
• Publication Date: 2018-04-20
• Subjects: Visual object detection -- vehicle detection -- binary descriptor -- feature fusion -- adaptive neuro-fuzzy inference system; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418770545
• Languages: English
• ISSNs: 1729-8806

Abstract

This article proposes a new preceding vehicle detection framework for challenging lighting environments using a novel feature fusion technique based on an adaptive neuro-fuzzy inference system. A combination of two feature descriptors, the histogram of oriented gradients and local binary patterns, is adopted to improve the vehicle detection accuracy of the proposed framework, and the performance of the combination in image transformations is evaluated. Furthermore, we tested the detection performance of the proposed framework in three challenging driving conditions and filmed the test image sequences for each categorized environment of the experiments. The experimental results demonstrate that the proposed framework outperforms the conventional framework under specific driving environments with harsh lighting conditions.