期刊文献+

多特征融合的车辆阴影消除 被引量:12

Vehicle shadow removal with multi-feature fusion
原文传递
导出
摘要 目的提出一种基于颜色特征和边缘特征相融合的算法,实现对复杂交通场景中车辆阴影的检测和消除。方法首先,通过经典混合高斯背景建模方法建立背景模型,以帧差法获取运动目标前景。其次,针对复杂多变的交通道路场景,采用串行融合策略检测车辆阴影。对运动目标前景基于边缘特征检测阴影之后,再进行RGB颜色特征方法检测阴影,此过程中利用边缘差分、形态学处理等运算以达到更好的阴影消除效果。为提高算法效率,对前景区域进行阴影评估,从而判断是否有必要进行阴影检测和消除。结果通过与统计参数法SP、统计非参数法SNP、两类判定性非模型法DNM1、DNM2等算法的对比,本文算法的阴影检测率和阴影识别率分别有大约10%的提升。实验结果表明,该算法能够有效消除车辆阴影,具有良好的准确性和鲁棒性。结论本文算法结合颜色和边缘两种特征,弥补基于单个特征方法的单一性,降低由于阴影区域边缘复杂、车辆颜色与阴影颜色相近等原因造成的阴影误检率,阴影消除效果良好。 Objective A novel algorithm that combines color feature and edge information is proposed to detect and remove vehicle shadows in complex traffic scenes. Method First, a background model is built with the classical Gaussian mixture background modeling method, and the moving vehicle foreground is obtained through frame difference. Second, a serial fusion strategy that combines color feature and edge information is applied to detect and eliminate vehicle shadows. Based on vehicle shadow detection by edge information method of the moving target foreground, the RGB color feature detection method is implemented to detect the shadow area further and to obtain a precise result. Edge difference and morphological pro- cessing methods are used during the operations to detect and eliminate shadows effectively. Shadow assessment is periodically evaluated on the foreground area to improve the efficiency of the algorithm by determining the necessity of applying the proposed algorithm. Result By comparision with SP, SNP, DNM1 and DNM2 algorithm, the proposed method realizes about 10% advance on shadow detection rate and shadow reeogmition rate. The high accuracy and robustness of the proposed shadow removal method are confirmed by the test results, and the effectiveness of the method is validated. Conclusion The proposed method that combines color feature and edge information outperforms those based on a single feature because of their unicity. In addition, the false detection rate caused by complex edges in shadow regions and color similarity between vehicles and shadows is effectively decreased.
出处 《中国图象图形学报》 CSCD 北大核心 2015年第3期311-319,共9页 Journal of Image and Graphics
基金 科技部国际合作专项(2012DFG11580) 国家自然科学基金项目(61003221) 中央高校基本科研业务费资助项目(0800219160)
关键词 颜色特征 边缘特征 多特征融合 阴影评估 阴影检测 阴影消除 color feature edge feature multifeature fusion shadow assessment shadow detection shadow removal
  • 相关文献

参考文献3

二级参考文献34

  • 1Hu Fuyuan Zhang Yanning Yao Lan Sun Jinqiu.A NEW METHOD OF MOVING OBJECT DETECTION AND SHADOW REMOVING[J].Journal of Electronics(China),2007,24(4):528-536. 被引量:3
  • 2BRADSKIG,KAEBLERA.学习OpenCV[M].于仕琪,刘瑞琪,译.北京:清华大学出版社,2009. 被引量:31
  • 3刘宏,李锦涛,刘群,钱跃良,李豪杰.融合颜色和梯度特征的运动阴影消除方法[J].计算机辅助设计与图形学学报,2007,19(10):1279-1285. 被引量:24
  • 4CHEUNG S, KAMATH C. Robust background sub- traction with foreground validation for urban traffic video [J]. Eurasip Journal on Applied Signal Process- ing, 2005, 2005(14): 2330-2340. 被引量:1
  • 5BRAHME Y B, KULKARNI P S. An implementation of moving object detection, tracking and counting ob- jects for traffic surveillance system [C] // Proceedings of International Conference on Computational Intelli- gence and Communication Systems. Piscataway, NJ, USA: IEEE, 2011: 143-148. 被引量:1
  • 6WEI L, NGUYEN A V, LEE E J. Real-time recogni- tion of humans by their walk [-J]. International Journal of Intelligent Information and Database Systems, 2011, 5(1): 24-38. 被引量:1
  • 7GOYETTE N, JODOIN P M, PORIKLI F, et al. Changedetection. net: a new change detection bench- mark dataset [-C]//Proceedings of IEEE Computer So- ciety Conference on Computer Vision and Pattern Rec- ognition Workshops. Piscataway, NJ, USA: IEEE, 2012 : 1-8. 被引量:1
  • 8Ist IEEE Change Detection Workshop. Change detec- tion net video database [-EB/OL]. [-2013-01-01]. ht-tp: ff wordpress-jodoin, dmi. usherb, ca/static/dataset/ dataset, zip. 被引量:1
  • 9HOFMANN M, TIEFENBACHER P, RIGOLL G. Background segmentation with feedback: the pixel- based adaptive segmenter [C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ, USA: IEEE, 2012: 38-43. 被引量:1
  • 10HERAS E R, SIKORA T. Complementary background models for the detection of static and moving objects in crowded environments [C] // Proceedings of IEEE In- ternational Conference on Advanced Video and Signal Based Surveillance. Piscataway, NJ, USA: IEEE, 2011: 71-76. 被引量:1

共引文献22

同被引文献135

  • 1王燕玲,李广伦,林晓.复杂动态环境下运动目标自动检测算法[J].系统仿真学报,2015,27(4):715-722. 被引量:11
  • 2AMATO A, HUERTA 1, MOZEROV M G, et al. Moving cast shad- ows detection methods for video surveillance applications[ M]. Hei- delberg: Springer Berlin. 2014:23-47. 被引量:1
  • 3AL-NAJDAWI N. BEZ H E, SINGHAI J, et al. A survey of cast shadow detection algorithms[ J]. Pattern Recognition Letters, 2012, 33(6): 752-764. 被引量:1
  • 4SANIN A, SANDERSON C, LOVELL B C. Shadow detection: a survey and comparative evaluation of recent methods [ J]. Pattern Recognition, 2012, 45(4) : 1684 - 1695. 被引量:1
  • 5AMATO A, MOZEROV M G, BAGDANOV A D, et al. Accuratemoving cast shadow suppression based on local color constancy de- tection[ J]. IEEE Transactions on Image Processing, 2011, 20 (10) : 2954 -2966. 被引量:1
  • 6RUSSELL M, ZOU J J, FANG G. Real-time vehicle shadow detec- tion[ J]. Electronics Letters. 2015, 51(16) : 1253 - 1255. 被引量:1
  • 7DAI J, QI M, WANG J, et al. Robust and accurate moving shadow detection based on multiple features fusion [ J]. Optics & Laser Technology, 2013, 54(26): 232-241. 被引量:1
  • 8MEHER S K, MURTY M N. Efficient method of moving shadow detection and vehicle classification [ J]. AEU - International Journal of Electronics and Communications, 2013, 67(8) : 665 - 670. 被引量:1
  • 9QI M, DAI J, ZHANG Q, et al. Cascaded cast shadow detection method in surveillance scenes[ J]. Optik - International Journal fir Light and Electron Optics, 2014, 125(3) : 1396 - 1400. 被引量:1
  • 10J1ANG K, LI A, CUI Z, et al. Adaptive shadow detection using global texture and sampling deduction[ J]. IET Computer Vision, 2013, 7(2): 115-122. 被引量:1

引证文献12

二级引证文献82

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部