收稿日期: 2017-02-24
网络出版日期: 2019-02-26
基金资助
国家自然科学基金资助项目(61376028);上海经济和信息化委员会资助项目(11XI-15)
Front-vehicle detection method based on monocular vision
Received date: 2017-02-24
Online published: 2019-02-26
针对前方车辆检测中遇到的速度和精度问题, 首先对感兴趣区域 (region-of-interest, ROI) 提取中大津阈值 Qtsu 分割方法应用范围的局限性进行了分析, 并考虑累计密度函数 (cumulative-density-function, CDF) 阈值分割方法鲁棒性差的问题, 提出了一种基于人工神经网络 (artificial neural network, ANN) 的方法来拟合阈值分割点, 以提高阈值分割的准确性和鲁棒性, 减少伪 ROI; 其次针对在复杂环境下车辆与背景粘连的问题提出了一种分区域六角分割的方法, 降低 ROI 漏检率; 最后对 ROI 提取 Haar 特征以及用优化的自适应提升 (adaptive boosting, AdaBoost) 对 ROI 进行分类, 得到筛选后的车辆目标. 实验结果表明, 对于分辨率为 640$\times $480 的视频, 系统处理速度可达到 81.3 帧/s, 错误率为 3.8%.
陈高攀, 徐美华, 王琪, 郭爱英 . 一种基于单目视觉的前方车辆检测算法[J]. 上海大学学报(自然科学版), 2019 , 25(1) : 56 -65 . DOI: 10.12066/j.issn.1007-2861.1889
To speed and improve accuracy of front-vehicle detection, limitations of Qtsu threshold and poor robustness of cumulative-density-function (CDF) threshold in the extraction of region-of-interest (ROI) are analyzed. An artificial neural network (ANN) method is used to fit threshold point to improve accuracy and robustness of the threshold and reduce false ROIs. A region-hexagon-segmentation method is presented to deal with overlap between vehicle region and background in a complicated environment for reducing misdetection. Haar features in the ROIs are extracted, and the ROIs are classified with an optimized adaptive boosting (AdaBoost) classifier to get real vehicle target. Experiments show that processing speed using the proposed method is 81.3 frames per second for a 640$\times $480 video with an error rate of 3.8%.
| [1] | Sivaraman S, Trivedi M M . Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(4):1773-1795. |
| [2] | Hilario C, Collado J M, Armingol J M , et al. Pyramidal image analysis for vehicledetection[C] //IEEE Proceedings of Intelligent Vehicles Symposium. 2005: 88-93. |
| [3] | 闫巧云 . 基于单目视觉与多特征的前方车辆检测算法研究[D]. 长沙: 中南大学, 2012. |
| [4] | Nuevo J, Parra I, Sj?berg J , et al. Estimating surrounding vehicles' pose using computer vision[C] //2010 13th International Conference on Intelligent Transportation Systems (ITSC). 2010: 1863-1868. |
| [5] | Viola P, Jones M . Rapid object detection using a boosted cascadeof simple features[C] //IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 511-518. |
| [6] | Dalal N, Triggs B . Histograms of oriented gradients for human detection[C] //IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005: 886-893. |
| [7] | Kanitkar A, Bharti B, Hivarkar U N . Vision based preceding vehicle detection using self shadows and structural edge features[C] //International Conference on Image Information Processing (ICIIP). 2011: 1-6. |
| [8] | Li X, Guo X . Vision-based method for forward vehicle detection and tracking[C] //International Conference on Mechanical and Automation Engineering (MAEE). 2013: 128-131. |
| [9] | 文学志, 方巍, 郑钰辉 . 一种基于类 Haar 特征和改进 AdaBoost 分类器的车辆识别算法[J]. 电子学报, 2011,5:1121-1126. |
| [10] | 毛健, 赵红东, 姚婧婧 . 人工神经网络的发展及应用[J]. 电子设计工程, 2011,24:62-65. |
| [11] | Lai H C, Savvides M, Chen T H . Proposed FPGA hardware architecture for hight frame rate ($>$100 fps) face detection using feature cascade classifiers[C] //6$^{\rm th}$ International Conference on Information, Communications and Signal. 2007: 1673-1677. |
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