An Intelligent Method for Moving Object Detection

  • Mihir Narayan Mohanty
  • Subhashree Rout
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)


Detection of activities of moving objects is a challenging problem for its promising applications. Emerging research topic on computer vision includes with detection on many applications to reduce the computation cost, simple and faster the object. In this paper, we present a motion control method for mobile robots in indoor environments based on color object detection. Probing over a digitized image of robots taken at top view to uniquely identify them is not quite an easy task. The recognition process involves scanning a digitized image and characterizing it, which is made difficult by varying illumination, position, and rotation. Furthermore, the vision system is plagued with inherent difficulties that cannot be completely controlled. Effects such as lighting and shadows, lens focus, and even quantum electrical effects in the sensor chip combine to make it essentially impossible to guarantee that the color being tracked down would remain constant as the robot traverses the exploration field. Among the different recognition cues, like shape, size, position, and motion, this paper focuses on color as the primary discriminating feature. After identification, the robots are operated wirelessly by interfacing with wireless module and motor driver.


Object detection Color detection Intelligent method Fuzzy logic 


  1. 1.
    Feris, R.S., Siddiquie, B., Petterson, J., Zhai, Y., Datta, A., Brown, L.M., Pankanti, S.: Large-scale vehicle detection, indexing, and search in urban surveillance videos. IEEE Trans. Multimedia 14(1), 28–42 (2012)CrossRefGoogle Scholar
  2. 2.
    Wu, B.-F., Juang, J.-H.: Adaptive vehicle detector approach for complex environments. IEEE Trans. Intell. Transp. Syst. 13(2), 817–827 (2012)CrossRefGoogle Scholar
  3. 3.
    Shan, M., Worrall, S., Nebot, E.: Probabilistic long-term vehicle motion prediction and tracking in large environments. IEEE Trans. Intell. Transp. Syst. 14(2), 539–552 (2013)CrossRefGoogle Scholar
  4. 4.
    Gopalan, R., Hong, T., Shneier, M., Chellappa, R.: A learning approach towards detection and tracking of lane markings. IEEE Trans. Intell. Transp. Syst. 13(3), 1088–1098 (2012)CrossRefGoogle Scholar
  5. 5.
    Zhu, J., Yuan, L., Zheng, Y.F., Ewing, R.L.: Stereo visual tracking within structured environments for measuring vehicle speed. IEEE Trans. Circuits Syst. Video Technol 22(10), 1471–1484 (2012)CrossRefGoogle Scholar
  6. 6.
    Dan, T., Lei, J., Yang, Y.: A robust approach for congested vehicles tracking based on tracking-model-detection framework. IEEE Trans. Circuits Syst. Video Technol. 23(10), 820–824 (2013)Google Scholar
  7. 7.
    Sakaino, H.: Video-based tracking, learning, and recognition method for multiple moving objects. IEEE Trans. Circuits Syst. Video Technol. 23(10), 1661–1674 (2013)CrossRefGoogle Scholar
  8. 8.
    O’Malley, R., Jones, E., Glavin, M.: Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. IEEE Trans. Intell. Transp. Syst. 11(2) (2010)Google Scholar
  9. 9.
    Cucchiara, R., Piccardi, M., Mello, P.: Image analysis and rule-based reasoning for a traffic monitoring system. IEEE Trans. Intell. Transp. Syst. 1(2), 119–130 (2000)CrossRefGoogle Scholar
  10. 10.
    Kar, S.K., Mohanty, M.N.: Statistical approach for color image detection. In: IEEE International Conference on Computer Communication and Informatics (ICCCI), 2013, pp. 1–4, 4–6Google Scholar
  11. 11.
    Mohanty, M.N., Kar, S.K., Mohanty, B.: Color detection and resistor evaluation in real time environment. In: IEEE International Conference on Control, Instrumentation, Energy and Communication, University of Calcutta, Calcutta, WB, India (2014)Google Scholar
  12. 12.
    Pattnaik, L., Mohanty, M.N., Mohanty, B.: An intelligent method for handoff decision in next generation wireless network, SEMCCO, In: Swarm, Evolutionary, and Memetic Computing, LNCS 7677, pp. 465–475, © Springer, Berlin (2013)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.ITERSikshya O Anusandhan UniversityBhubaneswarIndia
  2. 2.Department of Computer Science and Application, Vani ViharUtkal UniversityBhubaneswarIndia

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