Title: Efficient local adaptive thresholding for night-time vehicle candidate detection

Authors: Yeongyu Choi; Hyojin Lim; Cuong Nguyen Khac; Ju H. Park; Ho-Youl Jung

Addresses: Department of Information and Communication Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 38541, Korea ' Department of Information and Communication Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 38541, Korea ' Department of Information and Communication Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 38541, Korea ' Department of Electronic Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 38541, Korea ' Department of Information and Communication Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do, 38541, Korea

Abstract: In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, 'Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process?' For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.

Keywords: adaptive thresholding; vehicle detection; computer vision.

DOI: 10.1504/IJCVR.2017.086286

International Journal of Computational Vision and Robotics, 2017 Vol.7 No.5, pp.574 - 587

Available online: 03 Sep 2017 *

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