Moving Object Detection and Classification Using Neural Network

  • M. Ali Akber Dewan
  • M. Julius Hossain
  • Oksam Chae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)


Moving object detection and classification is an essential and emerging research issue in video surveillance, mobile robot navigation and intelligent home networking using distributed agents. In this paper, we present a new approach for automatic detection and classification of moving objects in a video sequence. Detection of moving edges does not require background; only three most recent consecutive frames are utilized. We employ a novel edge segment based approach along with an efficient edge-matching algorithm based on integer distance transformation, which is efficient considering both accuracy and time together. Being independent of background, the proposed method is faster and adaptive to the change of environment. Detected moving edges are utilized to classify moving object by using neural network. Experimental results, presented in this paper demonstrate the robustness of proposed method.


Video surveillance vision agent motion detection neural network 


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  1. 1.
    Radke, R., Andra, S., Al-Kohafi, O., Roysam, B.: Image Change Detection algorithms: A Systematic Survey. IEEE Transactions on Image Processing 14(3), 294–307 (2005)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Sogo, T., Ishiguro, H., Ishida, T.: Mobile Robot Navigation by a Distributed Vision System. Journal of New Generation Comp. 19(2), 121–138 (2001)zbMATHGoogle Scholar
  3. 3.
    DeSouza, G.N., Avinash, C.K.: Vision for Mobile Robot Navigation: A Survey. IEEE Transaction on PAMI 24(2), 237–267 (2002)Google Scholar
  4. 4.
    Lee, M., Kim, Y., Uhm, Y., Hwang, Z., Kim, G., Park, S., Song, O.: Location-Aware Multi-agent Based Intelligent Services in Home Networks. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 178–187. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Gutchess, D., Trajkovics, M., Cohen-Solal, E., Lyons, D., Jain, A.K.: A background model initialization algorithm for video surveillance. IEEE International Conference on Computer Vision 1, 733–740 (2001)Google Scholar
  6. 6.
    Kim, C., Hwang, J.N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Transaction on CSVT 12, 122–129 (2002)Google Scholar
  7. 7.
    Dailey, D.J., Cathey, F.W., Pumrin, S.: An algorithm to estimate mean traffic speed using un-calibrated cameras. IEEE Transactions on ITS 1(2), 98–107 (2000)Google Scholar
  8. 8.
    Hossain, M.J., Dewan, M.A.A., Chae, O.: Moving Object Detection for Real Time Video Surveillance: An Edge Segment Based Approach. IEICE Transaction on Communications E90-B(12), 3654–3664 (2007)CrossRefGoogle Scholar
  9. 9.
    Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transactions on PAMI 10(6), 849–865 (1988)Google Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Chichester (November 2000)Google Scholar
  11. 11.
    Lee, J., Cho, Y.K., Heo, H., Chae, O.S.: MTES: Visual Prgramming for Teaching and Research in Image Processing. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 1035–1042. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. Ali Akber Dewan
    • 1
  • M. Julius Hossain
    • 1
  • Oksam Chae
    • 1
  1. 1.Department of Computer EngineeringKyung Hee UniversityYongin-siSouth Korea

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