A Fast Mathematical Morphological Algorithm of Video-Based Moving Forklift Truck Detection in Noisy Environment

  • Vladimir O. Chernousov
  • Andrey V. SavchenkoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 436)


The problem of automatic detection of the moving forklift truck in video data is explored. This task is formulated in terms of computer vision approach as a moving object detection in noisy environment. It is shown that the state-of-the-art local descriptors (SURF, SIFT, FAST, ORB) are not characterized with satisfactory detection quality if the camera resolution is low, the lighting is changed dramatically and shadows are observed. In this paper we propose to use a simple mathematical morphological algorithm to detect the presence of a cargo on the forklift truck. Its first step is the estimation of the movement direction and the front part of the truck by using the updating motion history image. The second step is the application of Canny contour detection and binary morphological operations in front of the moving object to estimate simple geometric features of empty forklift. The algorithm is implemented with the OpenCV library. Our experimental study shows that the best results are achieved if the difference of the width of bounding rectangles is used as a feature. Namely, the detection accuracy is 78.7 % (compare with 40 % achieved by the best local descriptor), while the average frame processing time is only 5 ms (compare with 35 ms for the fastest descriptor).


Object detection Video-based recognition Noisy environment Motion history image SURF Binary morphology Canny operator Forklift truck detection 



Andrey Savchenko is supported by RSF (Russian Science Foundation) grant 14-41-00039.


  1. 1.
    Shapiro, L., Stockman, G.: Computer Vision. Prentice Hall, Upper Saddle River (2001)Google Scholar
  2. 2.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 4th edn. Cengage Learning, Stamford (2014)Google Scholar
  3. 3.
    ISS (Intelligence Secure Systems).
  4. 4.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    Savchenko, A.V.: Probabilistic neural network with homogeneity testing in recognition of discrete patterns set. Neural Netw. 46, 227–241 (2013)CrossRefzbMATHGoogle Scholar
  7. 7.
    Savchenko, A.V.: Adaptive video image recognition system using a committee machine. Opt. Memory Neural Netw. (Inf. Opt.) 21(4), 219–226 (2012)CrossRefGoogle Scholar
  8. 8.
    Chien, S.-Y., Ma, S.-Y., Chen, L.-G.: Efficient moving object segmentation algorithm using background registration technique. IEEE Trans. Circuits Syst. Video Technol. 12(7), 577–586 (2002)CrossRefGoogle Scholar
  9. 9.
    Neria, A., Colonnese, S., Russo, G., Talone, P.: Automatic moving object and background separation. Sig. Process. 66(2), 219–232 (1998)CrossRefGoogle Scholar
  10. 10.
    Ahad, M.A.R., Tan, J.K., Kim, H., Ishikawa, S.: Motion history image: its variants and applications. Mach. Vis. Appl. 23(2), 255–281 (2012)CrossRefGoogle Scholar
  11. 11.
    Najman, L., Talbot, H. (eds.): Mathematical Morphology: From Theory to Applications. Wiley-ISTE, New York (2010)Google Scholar
  12. 12.
  13. 13.
    Canny, J.A.: Computational Approach to Edge Detection, pp. 679–698. IEEE Computer Society (1986)Google Scholar
  14. 14.
  15. 15.
    Savchenko, A.V.: Directed enumeration method in image recognition. Pattern Recogn. 45(8), 2952–2961 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsNizhniy NovgorodRussian Federation

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