Abstract
The article is devoted to the problem of moving object detection on a video stream. One of the most important problems in this case is unstable background with moving elements that leads to increasing the level of misoperations while selecting moving objects on the scenes with background objects that change with the speed comparable with the speed of movement of object of interest through the image. Two types of dynamic background elements are considered: moving clouds and waving trees. Well known algorithms based on Gaussian mixture model, interest points detection approach (FAST, Harris, MinEigen and SURF) and optical flow (Farneback, Horne-Shunk and Lukas-Kanade) approaches are examined. Farneback based optical flow method is the most appropriate for this task. The new image series processing method is proposed in the paper and tested on the same examples with the background formed with moving clouds and waving trees. The results allow to conclude that it is good in case of moving clouds but its drawback is false detection in areas of waving trees. Future work requires a more advanced prediction model that can consider complicated trajectories of background fragments movement. #CSOC1120.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tekalp, A.: Digital Video Processing. Prentice Hall, USA (2015)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: 17th IEEE International Conference on Pattern Recognition (ICPR 2004), vol. 2, pp. 28–31. IEEE, USA (2004)
Ko, T., Soatto, S., Estrin, D.: Background subtraction on distributions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 276–289. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88690-7_21
Yu, X., Chen, X., Jiang, M.: Motion detection in moving background using a novel algorithm based on image features guiding self-adaptive sequential similarity detection algorithm. Optik – Int. J. Light Electr. Optics 123(22), 2031–2037 (2012)
Ramya, R., Sudhakara, B.: Motion detection in moving background using ORB feature matching and affine transform. Int. J. Innov. Technol. Res. 1(1), 162–164 (2015)
Patel, M.P., Parmar, S.K.: Moving object detection with moving background using optic flow. In: IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), pp. 1–6. IEEE, USA (2014)
Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: 10th IEEE International Conference on Computer Vision (ICCV 2005), vol. 2, pp. 1508–1511. IEEE, USA (2005)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference (AVC-1988), pp. 1–6. Alvety Vision Club, Great Britain (1988)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the IEEE Computer Society Conference on Computer vision and pattern recognition (CVPR 1994), pp. 593–600. IEEE, USA (1994)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol. 3951, pp. 404–417. Springer, Berlin, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: 7th International Joint Conference on Artificial Intelligence (IJCAI 1981), vol. 2, pp. 674–679. International Joint Conference on Artificial Intelligence, USA (1981)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Stulajter, F.: Predictions in Time Series Using Regression Models. Springer, New York (2002)
Bruce, A., Bruce, P.: Practical Statistics for Data Scientists. O’Reilly Media, USA (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Teplitskiy, E., Mitrokhin, M., Zakharov, S., Kuzmin, A., Mitrokhina, N., Sazykina, V. (2021). Video Processing Method for Moving Objects Detection on Scenes with Complex Dynamic Background. In: Silhavy, R. (eds) Informatics and Cybernetics in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-030-77448-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-77448-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77447-9
Online ISBN: 978-3-030-77448-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)