[1]朱周,路小波,卫朋,等.基于超像素和支持向量机的车辆阴影检测算法[J].东南大学学报(自然科学版),2015,45(3):443-447.[doi:10.3969/j.issn.1001-0505.2015.03.006]
 Zhu Zhou,Lu Xiaobo,Wei Peng,et al.Vehicle shadow detection algorithm based on superpixel and SVM[J].Journal of Southeast University (Natural Science Edition),2015,45(3):443-447.[doi:10.3969/j.issn.1001-0505.2015.03.006]
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基于超像素和支持向量机的车辆阴影检测算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第3期
页码:
443-447
栏目:
交通运输工程
出版日期:
2015-05-20

文章信息/Info

Title:
Vehicle shadow detection algorithm based on superpixel and SVM
作者:
朱周13路小波23卫朋23曾维理23
1东南大学交通学院, 南京 210096; 2东南大学自动化学院, 南京 210096; 3东南大学复杂工程系统测量与控制教育部重点实验室, 南京 210096
Author(s):
Zhu Zhou13 Lu Xiaobo23 Wei Peng23 Zeng Weili23
1School of Transportation, Southeast University, Nanjing 210096, China
2School of Automation, Southeast University, Nanjing 210096, China
3 Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
关键词:
阴影检测 超像素 支持向量机 车辆检测
Keywords:
shadow detection superpixel support vector machine vehicle detection
分类号:
U491.1
DOI:
10.3969/j.issn.1001-0505.2015.03.006
摘要:
为解决车辆阴影检测中易将车辆阴影相似的车辆区域误检测为车辆阴影的问题,提出了一种基于超像素和支持向量机的车辆阴影检测算法.首先,利用简单线性迭代聚类法将图像分割为若干超像素;然后,以超像素为基本检测单位,根据HSV空间中的一组判别条件对车辆阴影进行初步检测;在此基础上,利用支持向量机识别并去除被误检测为车辆阴影的车辆区域,进而得到最终的车辆阴影.实验结果表明,所提算法能够较好地区分车辆阴影及与车辆阴影相似的车辆区域,提高车辆阴影的检测率和分类率.
Abstract:
To solve the problem that the vehicle region similar to the shadow is apt to be wrongly detected as the shadow during vehicle shadow detection, a vehicle shadow detection algorithm based on superpixel and SVM(support vector machine)is proposed. First, the current image is segmented to several superpixels by the simple linear iterative clustering method. Then, according to a group of criterion conditions in HSV(hue, saturation, value)space, the vehicle shadow is detected preliminary by taking superpixels as basic testing units. Finally, the vehicle region which is detected wrongly as the vehicle shadow is recognized by SVM and removed from the preliminary detection results, thus the final vehicle shadow is obtained. The experimental results show that the proposed method can well distinguish the shadow from the vehicle region similar to the shadow, and can improve the detection rate and the discrimination rate of the vehicle shadow.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2014-11-10.
作者简介: 朱周(1984—),男,博士生;路小波(联系人),男,博士,教授,博士生导师,xblu2013@163.com.
基金项目: 国家自然科学基金资助项目(61374194,61403081).
引用本文: 朱周,路小波,卫朋,等.基于超像素和支持向量机的车辆阴影检测算法[J].东南大学学报:自然科学版,2015,45(3):443-447. [doi:10.3969/j.issn.1001-0505.2015.03.006]
更新日期/Last Update: 2015-05-20