Video Surveillance Based Tracking System

  • R. Venkatesan
  • P. Dinesh Anton Raja
  • A. Balaji GaneshEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


The paper presents video surveillance-based tracking system for the outdoor environment. The video processing has been done using both LabVIEW and MATLAB, and the comparisons are illustrated. In LabVIEW, self-learning and real-time moving object tracking are implemented for the surveillance of environment. In a two-dimensional color pattern matching, a self-learned template has to be located on real-time video, regardless of the template’s position, color, and shape. It is done by organizing a set of feature vectors that encompass all the variations in the self-learned template. Matching is then done by determining the best similarity between the feature vectors extracted from the video image and the self-learned template set. Template learning time and elapsed time are taken as parameters for the comparison. In MATLAB, tracking is achieved using Camshift algorithm. The feature vectors include position, orientation, and size of the object to be tracked.


Camshift algorithm Self-autonomous video surveillance Tracking system Region of interest LabVIEW and MATLAB 



The authors wish to thank Department of Science and Technology for awarding a project under Cognitive Science Initiative Programme (DST File No.: SR/CSI/09/2011) through which the work has been implemented.


  1. 1.
    G. Sharma, S. Sood, G.S. Gaba, N. Gupta, Image recognition system using geometric matching and contour detection. Proc. Int. J. Comput. Appl. 51(17), 48–53 (2012)Google Scholar
  2. 2.
    P. Armen, Vision system for disabled people using pattern matching algorithm. In Proceedings of the Seventh International Conference on Computer Science and Information Technologies. (2009), pp. 343–346Google Scholar
  3. 3.
    A. Salhi, A.Y. Jammoussi, Object tracking system using Camshift, meanshift and kalman filter. World Academy of Science Engineering and Technology. (2012)Google Scholar
  4. 4.
    S. Avidan, Support vector tracking. in IEEE Conference on Computer Vision and Pattern Recognition. 8, 184–191 (2001)Google Scholar
  5. 5.
    G. Bradski, T. Ogiuchi, M. Higashikubo, Visual tracking algorithm using pixel-pair feature. Int. Conf. Pattern Recogn. 4, 1808–1811 (2010)Google Scholar
  6. 6.
    Y. Ruiguo, Z. Xinrong, The design and implementation of face tracking in real time multimedia recording system. IEEE Trans. 3, 1–3 (2009)Google Scholar
  7. 7.
    E. David, B. Erich, K. Daniel, S. Anselm, Fast and robust Camshift tracking. IEEE Trans. 8, 1–8 (1982)Google Scholar
  8. 8.
    B. Wang, Q. Yang, C. liu, M. Cui, An efficient method for face feature extraction based on contourlet transform and fast independent component analysis. 4th International Symposium on Computational Intelligence and Design. (2011), pp. 344–347Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • R. Venkatesan
    • 1
  • P. Dinesh Anton Raja
    • 1
  • A. Balaji Ganesh
    • 1
    Email author
  1. 1.Department of TIFAC COREVelammal Engineering CollegeChennaiIndia

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