70 Harris SIFT Harris SIFT HSV hue saturation value Harris SIFT RGB red green blue HSV 11 1 if α I v x y B v x y β shadow x y = 1 &I s x y - B s x y t

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1 Vol No 3 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Jun 12 Harris SIFT Harris SIFT SIFT 2 /3 Harris SIFT TP391 A Harris Corner and SIFT Feature of Vehicle and Type Recognition KANG Wei-xin 1 CAO Yu-ting 1 SHENG Zhuo 1 LI Peng 2 JIANG Peng 2 1 College of Information and Communication Engineering Harbin Engineering University Harbin 0001 China 2 Heilongjiang Hashuang Road Management Office Harbin 0090 China Abstract Considering to the important factors of accuracy and real-time performance can not meet the requirement simultaneously in current vehicle recognition a compound image matching model and a recognition method were developed namely classifying vehicles firstly by utilizing Harris corner then classifying vehicles detailedly by using SIFT feature Compared with the one used SIFT feature only the method shortens the time of disposal by 2 / 3 under guaranteeing the accuracy to be kept essentially constant real-time performance was improved greatly Key words vehicle recognition Harris corner SIFT feature image matching SIFT scale invariant feature transform 48 intelligence transport system ITS Harris 7 Moravec Harris SIFT yutin102@ 3 com

70 Harris SIFT Harris SIFT HSV hue saturation value Harris SIFT RGB red green blue HSV 11 1 if α I v x y B v x y β shadow x y = 1 &I s x y - B s x y ts 1 9 & I h x y - B h x y th 0 else 2 1 I h x y I

2 70 Harris SIFT Harris SIFT HSV hue saturation value Harris SIFT RGB red green blue HSV 11 1 if α I v x y B v x y β shadow x y = 1 &I s x y - B s x y ts 1 9 & I h x y - B h x y th 0 else 2 1 I h x y I s x y I v x y HSV H 混合高斯模型建立背景视频获取背景差分移动区域获取利用 HSV 空间检测阴影并去除完成车辆提取取无车一帧彩色图像为背景 S V B h x y B s x y B v x y HSV H S V ts th α = 0 1 β = a b c 3 待识别车辆 Harris 特征提取样本车辆 Harris 特征提取利用 Hausdorff 距离进行车型初步分类待识别车辆 SIFT 特征提取样本车辆 SIFT 特征提取 (a) 阴影检测背景图利用特征匹配对车型再分类识别结果 (b) 阴影检测原图 Stauffer 10 (c) 检测结果 2

3 Harris SIFT 71 2 3 3 8 1 1 2 Harris Harris 3 Harris Hausdorff Harris R 2 R = det C - ktr 2 C 2 SIFT SIFT C x = I 2 u x I uv x [ I uv x I 2 v x ] I u x I uv x I uv x I v x x u v k 0 4 0 6 Harris R T

3 3 Harris SIFT Harris Harris 3 Harris Hausdorff Harris R 2 R = det C - ktr 2 C 2 SIFT SIFT C x = I 2 u x I uv x [ I uv x I 2 v x ] I u x I uv x I uv x I v x x u v k Harris R T Harris 4 4 Harris 5 SIFT DOG difference 目标车辆小型车样本中型车样本 Harris 角点 Harris 角点 Harris 角点 of Gaussian DOG 建立角点图建立角点图 D x y σ = G x y kσ - G x y σ I x y 3 G x y kσ σ I x y L L x y kσ = G x y kσ I x y 4 根据最小值判定类型 5 Hausdorff m x y = L x +1 y - L x -1 y 2 2 槡 + L x y +1 - L x y SIFT 5 SIFT θ x y = arctan ( L x y L x y - 1 L x + 1 y - L x - 1 y ) 6 SIFT 8 1 SUV 2 1 Hausdorff Hausdorff 12 Hausdorff Harris Hausdorff 建立角点图依次计算与 3 种样本的 Harsdorff 距离样本库建立大型车样本 Harris 角点建立角点图 d 1 d 2

初判定类别中样本 SIFT 特征是 2 2 3 Core i3 2 2GHz CPU 2GB VS10 OpenCV2 2 3 Harris 56 8%

4 72 d 1 /d 2 Harris SIFT Harris Hausdorff SIFT SIFT 3 2 Harris 1 79 SUV SIFT 1 / 待测目标 SIFT 特征满足再分类条件? 初判定类别中样本 SIFT 特征是 Core i3 2 2GHz CPU 2GB VS10 OpenCV2 2 3 Harris 56 8% SIFT SIFT 85 6% % Harris Harris 0 95 SIFT /s 7 Harris 满足再分类条件? SIFT 否 7 a 小型车根据匹配点数 7 b 判定车型 6 SIFT 2 7 (a) (b)

5 3 Harris SIFT 73 SIFT 4% Harris SIFT 3 Harris 3 初分类车型小型车中型车大型车车型细分类车型轿车 SUV 小货车中型货柜车一般中型货车罐式车大型货柜车一般大型货车 3 样本总数 8 基于 Harris 角点方法的正确识别数基于 SIFT 特征方法的正确识别数本文方法初分类的正确识别数本文方法细分类的正确识别数 Harris SIFT Harris SIFT - 1 SIFT J on Computer Vision and Pattern Recognition Fort Collins J J LI Jian ZHAO Wang-zi GUO Hui Vehicle Type Recognition Based on Harris Corner Detector C / /Proceedings of the 2nd International Conference on Transportation Engineering ICTE Chengdu China Harris J D HARRIS C STEPHENS M A Combined Corner and Edge Detector C / /Proceedings of the Fourth Alvey Vision Conference Manchester the University of Sheffield Printing Unit LOWE D Distinctive Image Features from Scale-invariant Keypoints J International Journal of Computer Vision XIA Li-min Vehicle Shape Recovery and Recognition Using Generic Models C / /Proceedings of the 4th World Congress on Intelligent Control and Automation Shanghai China STAUFFER C GRIMSON W Adaptive Background Mixture Models for Real-time Tracking C / /Proceedings of IEEE Conference CUCCHIARA R GRANA C PICCARDI M et al Improving Shadow Suppression in Moving Object Detection with HSV Color Information C / /Proceedings of IEEE Intelligent Transportation Systems Conference Oakland US Hausdorff J

1 引言

1 引言 P P 第 40 卷 Vol.40 第 7 期 No.7 计算机工程 Computer Engineering 014 年 7 月 July 014 开发研究与工程应用文章编号 :1000-348(014)07-081-05 文献标识码 :A 中图分类号 :TP391.41 摘基于图像识别的震象云地震预测方法谢庭,