Shadow removal algorithm for moving vehicles based on Multi-feature Fusion
[1]Wu Guodong,Zhang Aimei,Huang Xiao,et al.Shadow removal algorithm for moving vehicles based on Multi-feature Fusion[J].Journal of Zhengzhou University (Engineering Science),2019,40(06):79-83.[doi:10.13705/j.issn.1671-6833.2019.03.012]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 06
Page number:
79-83
Column:
Public date:
2019-11-16
- Title:
- Shadow removal algorithm for moving vehicles based on Multi-feature Fusion
- Keywords:
- vehicle detection; shadow removal; edge detection; HSI; LBP
- CLC:
- TP391
- DOI:
- 10.13705/j.issn.1671-6833.2019.03.012
- Abstract:
- In traffic surveillance video, it is necessary to detect the information of moving vehicles, however, the shadow of the vehicle is often wrongly detected as the vehicle itself, which directly affects the accuracy of the vehicle detection and brings difficulties to the technical processing behind the vehicle detection .In this paper, a vehicle shadow removal method based on color space, texture feature and edge feature is proposed. First, the background model is established by the traditional mixed gaussian method, and the foreground target is extracted.Second The shadow of the foreground target is detected by threshold method in HSI (Hue-Saturation-Intensity) space,because the detection based on color model is easy to detect the moving object itself as shadow, the LBP operator combined with edge feature detection method is used to extract the moving target.Finally, combining the foreground target detected by LBP operator and edge feature with the shadow object detected in HSI color space, the actual shadow region can be detected.The real moving foreground target can be obtained by removing the detected shadow area. The simulation results show that the proposed algorithm can effectively remove the shadow of moving vehicle in traffic surveillance video, and improve the detection rate of moving vehicle shadow by about 10%
- References:
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Last Update:
2019-11-25