Abstract
Reference update to adapt with the dynamism of environment is one of the most challenging tasks in moving object detection for video surveillance. Different background modeling techniques have been proposed. However, most of these methods suffer from high computational cost and difficulties in determining the appropriate location as well as pixel values to update the background. In this paper, we present a new algorithm which utilizes three most recent successive frames to isolate moving edges for moving object detection. It does not require any background model. Hence, it is computationally faster and applicable for real time processing. We also introduce segment based representation of edges in the proposed method instead of traditional pixel based representation which facilitates to incorporate an efficient edge-matching algorithm to solve edge localization problem. It provides robustness against the random noise, illumination variation and quantization error. Experimental results of the proposed method are included in this paper to compare with some other standard methods that are frequently used in video surveillance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Radke, R., Andra, S., Al-Kohafi, O., Roysam, B.: Image Change Detection Algorithms: A Systematic Survey. IEEE Trans. on Image Processing 14(3), 294–307 (2005)
Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A Survey of Video Processing Techniques for Traffic Applications. Image and Vision Computing 21(4), 359–381 (2003)
Chien, S.Y., Ma, S.Y., Chen, L.: Efficient Moving Object Segmentation Algorithm Using Background Registration Technique. IEEE Transactions on Circuits and Systems for Video Technology 12(7), 577–586 (2002)
Sappa, A.D., Dornaika, F.: An Edge-Based Approach to Motion Detection. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2006. LNCS, vol. 3991, pp. 563–570. Springer, Heidelberg (2006)
Gutchess, D., Trajkovics, M., Cohen-Solal, E., Lyons, D., Jain, A.K.: A Background Model Initialization Algorithm for Video Surveillance. Proc. of IEEE Intl. Conf. on Computer Vision 1, 733–740 (2001)
Kim, C., Hwang, J.N.: Fast and Automatic Video Object Segmentation and Tracking for Content-based Applications. IEEE Trans. on Circuits and Systems for Video Tech. 12, 122–129 (2002)
Dailey, D.J., Cathey, F.W., Pumrin, S.: An Algorithm to Estimate Mean Traffic Speed using Un-calibrated Cameras. IEEE Trans. on Intelligent Transportation Sys. 1(2), 98–107 (2000)
Makarov, A., Vesin, J.M., Kunt, M.: Intrusion Detection Using Extraction of Moving Edges, International Conf. on. Pattern Recognition 1, 804–807 (1994)
Ahn, K.O., Hwang, H.J., Chae, O.S.: Design and Implementation of Edge Class for Image Analysis Algorithm Development based on Standard Edge. In: Proc. of KISS Autumn Conference, pp. 589–591 (2003)
Borgefors, G.: Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm. IEEE Trans. on PAMI 10(6), 849–865 (1988)
Canny, J.: A Computational Approach to Edge Detection. IEEE Trans. on PAMI 8(6), 679–698 (1986)
Lee, J., Cho, Y.K., Heo, H., Chae, O.S.: MTES: Visual Programming for Teaching and Research in Image Processing. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 1035–1042. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dewan, M.A.A., Hossain, M.J., Chae, O. (2007). Reference Independent Moving Object Detection: An Edge Segment Based Approach. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_62
Download citation
DOI: https://doi.org/10.1007/978-3-540-74819-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74817-5
Online ISBN: 978-3-540-74819-9
eBook Packages: Computer ScienceComputer Science (R0)