Abstract
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).
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References
Shapiro, L., Stockman, G.: Computer Vision. Prentice Hall, Upper Saddle River (2001)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 4th edn. Cengage Learning, Stamford (2014)
ISS (Intelligence Secure Systems). http://www.iss.ru/
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Savchenko, A.V.: Probabilistic neural network with homogeneity testing in recognition of discrete patterns set. Neural Netw. 46, 227–241 (2013)
Savchenko, A.V.: Adaptive video image recognition system using a committee machine. Opt. Memory Neural Netw. (Inf. Opt.) 21(4), 219–226 (2012)
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)
Neria, A., Colonnese, S., Russo, G., Talone, P.: Automatic moving object and background separation. Sig. Process. 66(2), 219–232 (1998)
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)
Najman, L., Talbot, H. (eds.): Mathematical Morphology: From Theory to Applications. Wiley-ISTE, New York (2010)
ISS video dataset. ftp://isstemp:isstemp@ftpsupport.iss.ru/Loaders/Video/
Canny, J.A.: Computational Approach to Edge Detection, pp. 679–698. IEEE Computer Society (1986)
OpenCV library. http://opencv.willowgarage.com/wiki/
Savchenko, A.V.: Directed enumeration method in image recognition. Pattern Recogn. 45(8), 2952–2961 (2012)
Acknowledgements
Andrey Savchenko is supported by RSF (Russian Science Foundation) grant 14-41-00039.
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Chernousov, V.O., Savchenko, A.V. (2014). A Fast Mathematical Morphological Algorithm of Video-Based Moving Forklift Truck Detection in Noisy Environment. In: Ignatov, D., Khachay, M., Panchenko, A., Konstantinova, N., Yavorsky, R. (eds) Analysis of Images, Social Networks and Texts. AIST 2014. Communications in Computer and Information Science, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-319-12580-0_5
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