A Novel Moving Object Detection Algorithm of the Monitor Video in the Foggy Weather

  • Chunyu Xu
  • Yufeng WangEmail author
  • Wenyong Dong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


In severe weather, the traditional three-frame difference method is prone to “hole” phenomenon in the moving object detection of the monitor video. In order to solve this problem, a novel moving object detection algorithm (MOD-DT) is proposed, which is combining dark color prior and oriented filtering. MOD-DT first detects the foggy image of the Monitor video, then de-haze the foggy image by dark primary color, and finally detects the moving object in the Monitor video image by the three-frame difference algorithm. Thus, MOD-DT can reduce the impact of the severe weather on the moving object detection. The experimental results show that this algorithm is superior to the traditional moving object detection algorithm in terms of integrity and accuracy, and can realize fast moving object extraction in the complex background environment.


Moving object detection Three-frame difference De-haze 



This research was supported by the National Natural Science Foundation of China (Nos. 61672024, 61170305 and 60873114), and National Key R&D Program of China (No. 2018YFB0904200), and the Key Research Program in Higher Education of Henan (No. 17A520046), and the Research on Application Foundation and Advanced Technology Program of Nanyang (No. JCQY2018012), and the Research on Education and Teaching Reform Program of NYIST (Nos. NIT2017JY-001 and NIT2017JY-032).


  1. 1.
    Kim, J., Ye, G., Kim, D.: Moving object detection under free-moving camera. In: IEEE International Conference on Image Processing, pp. 4669–4672 (2010)Google Scholar
  2. 2.
    Akinlar, C., Topal, C.: EDPF: a real-time parameter-free edge segment detector with a false detection control. Int. J. Pattern Recognit. Artif. Intell. 26(01), 898–915 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1719 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Archetti, F., Manfredotti, C.E., Messina, V., Sorrenti, D.G.: Foreground-to-ghost discrimination in single-difference pre-processing. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 263–274. Springer, Heidelberg (2006). Scholar
  5. 5.
    Sengar, S.S., Mukhopadhyay, S.: A novel method for moving object detection based on block based frame differencing. In: International Conference on Recent Advances in Information Technology, pp. 10–23 (2016)Google Scholar
  6. 6.
    Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., Yan, S.: Deep joint rain detection and removal from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1357–1366 (2017)Google Scholar
  7. 7.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  8. 8.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)CrossRefGoogle Scholar
  9. 9.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer and Information EngineeringNanyang Institute of TechnologyNanyangChina
  2. 2.Computer SchoolWuhan UniversityWuhanChina
  3. 3.Software SchoolNanyang Institute of TechnologyNanyangChina

Personalised recommendations