计算机与现代化

• 图像处理 • 上一篇    下一篇

基于多特征融合的车辆阴影检测与去除

  

  1. (河海大学能源与电气学院,江苏南京211100)
  • 收稿日期:2019-05-15 出版日期:2019-12-11 发布日期:2019-12-11
  • 作者简介:王威(1995-),男,江苏扬州人,硕士研究生,研究方向:机器视觉与图像处理,E-mail: 15295798362@163.com; 李志华(1965-),男,江苏兴化人,教授,硕士生导师,博士,研究方向:人工智能与复杂系统故障诊断,E-mail: zhli@hhu.edu.cn。
  • 基金资助:
    江苏省自然科学基金资助项目(BK20151500)

Vehicle Shadow Detection and Removal Based on Multi-feature Fusion

  1. (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)
  • Received:2019-05-15 Online:2019-12-11 Published:2019-12-11

摘要: 基于ViBE目标检测算法,融合交通监控视频中车辆的边缘与颜色特征,提出一种基于多特征融合的算法,实现对复杂交通场景中车辆阴影的检测与去除。通过ViBE提取前景目标,采用串行融合方式检测阴影。首先在传统的基于边缘特征检测阴影的基础上,利用水平集方法代替水平垂直填充,实现多个前景目标内部边缘的快速填充。在获取候选的阴影区域后,结合HSV颜色特征以及形态学处理等操作,以达到更好的阴影去除效果。通过对不同的视频图像序列进行测试,表明提出的多特征融合算法能有效去除投射阴影,且优于单个特征方法,适用于复杂的交通场景。

关键词: ViBE, 边缘特征, 颜色特征, 多特征融合, 水平集, HSV, 阴影去除

Abstract: Based on the ViBE algorithm and the edge and color characteristics of vehicles in traffic surveillance video, a multi-feature fusion algorithm is proposed to detect and remove vehicle shadows in complex traffic scenes. The method uses ViBE to extract foreground targets, and detects shadow by serial fusion strategy. Firstly, on the basis of the traditional edge-based shadow detection, the level set method is used to achieve rapid filling of inner edges of multiple foreground targets instead of horizontal and vertical operation. After obtaining candidate shadow regions, the HSV color feature and the morphological processing are combined to remove shadow perfectly. Tests with different video image sequences show that the proposed multi-feature fusion algorithm can effectively remove cast shadows and is superior to methods based on a single feature, which is suitable for complex traffic scenes.

Key words: ViBE, edge feature, color feature, multi-feature fusion, level set, HSV, shadow removal

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