中文

Journal of Shanghai University >

2019 , Vol. 25 >Issue 1: 56 - 65

DOI: https://doi.org/10.12066/j.issn.1007-2861.1889

Research Articles

Front-vehicle detection method based on monocular vision

Expand
  • 1. Microelectronic Research and Development Center, Shanghai University, Shanghai 200444, China
    2. School of Mechatronic Engineering and Automation, Shanghai University,Shanghai 200444, China

Received date: 2017-02-24

  Online published: 2019-02-26

Fold

Abstract

To speed and improve accuracy of front-vehicle detection, limitations of Qtsu threshold and poor robustness of cumulative-density-function (CDF) threshold in the extraction of region-of-interest (ROI) are analyzed. An artificial neural network (ANN) method is used to fit threshold point to improve accuracy and robustness of the threshold and reduce false ROIs. A region-hexagon-segmentation method is presented to deal with overlap between vehicle region and background in a complicated environment for reducing misdetection. Haar features in the ROIs are extracted, and the ROIs are classified with an optimized adaptive boosting (AdaBoost) classifier to get real vehicle target. Experiments show that processing speed using the proposed method is 81.3 frames per second for a 640$\times $480 video with an error rate of 3.8%.

Key words： vehicle detection; region-of-interest (ROI); artificial neural network (ANN); Haar; adaptive boosting (AdaBoost)

Cite this article

CHEN Gaopan, XU Meihua, WANG Qi, GUO Aiying . Front-vehicle detection method based on monocular vision[J]. Journal of Shanghai University, 2019 , 25(1) : 56 -65 . DOI: 10.12066/j.issn.1007-2861.1889

References

[1] Sivaraman S, Trivedi M M . Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(4):1773-1795.
[2] Hilario C, Collado J M, Armingol J M , et al. Pyramidal image analysis for vehicledetection[C] //IEEE Proceedings of Intelligent Vehicles Symposium. 2005: 88-93.
[3] 闫巧云 . 基于单目视觉与多特征的前方车辆检测算法研究[D]. 长沙: 中南大学, 2012.
[4] Nuevo J, Parra I, Sj?berg J , et al. Estimating surrounding vehicles' pose using computer vision[C] //2010 13th International Conference on Intelligent Transportation Systems (ITSC). 2010: 1863-1868.
[5] Viola P, Jones M . Rapid object detection using a boosted cascadeof simple features[C] //IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 511-518.
[6] Dalal N, Triggs B . Histograms of oriented gradients for human detection[C] //IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005: 886-893.
[7] Kanitkar A, Bharti B, Hivarkar U N . Vision based preceding vehicle detection using self shadows and structural edge features[C] //International Conference on Image Information Processing (ICIIP). 2011: 1-6.
[8] Li X, Guo X . Vision-based method for forward vehicle detection and tracking[C] //International Conference on Mechanical and Automation Engineering (MAEE). 2013: 128-131.
[9] 文学志, 方巍, 郑钰辉 . 一种基于类 Haar 特征和改进 AdaBoost 分类器的车辆识别算法[J]. 电子学报, 2011,5:1121-1126.
[10] 毛健, 赵红东, 姚婧婧 . 人工神经网络的发展及应用[J]. 电子设计工程, 2011,24:62-65.
[11] Lai H C, Savvides M, Chen T H . Proposed FPGA hardware architecture for hight frame rate ($>$100 fps) face detection using feature cascade classifiers[C] //6$^{\rm th}$ International Conference on Information, Communications and Signal. 2007: 1673-1677.
Options
Outlines

/

〈 〉