Analytics Based on Video Object Tracking for Surveillance

  • Nagaraj Bhat
  • U. Eranna
  • B. M. Mahendra
  • Savita Sonali
  • Adokshaja Kulkarni
  • Vikhyath Rai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


An abandoned object in public places is one of the typical surveillance breaches. Detecting an abandoned object in surveillance video is very important to forecast terrorist activity. This work aims to develop a modular system with several individual stages where in at each stage different algorithm is employed. The overall task is to detect abandoned object in a video stream. This has been implemented in Math Work’s MATLAB integrated development environment. The performance of the system is evaluated on test videos from standard publically available datasets and also custom dataset. The Abandoned Object Detection system is tested for two different datasets—publically available i-LiDS AVSS and custom dataset. The metric called system performance used to evaluate our system provided 85.71% result for AVSS dataset and 75% for custom dataset, with overall system performance reaching up to 78.125%.




  1. 1.
    C. Stauffer and W. E. L. Grimson, ― “Adaptive background mixture models for real-time tracking, in Proc. IEEE Conference on Computer Vision and Pattern Recognition”, Vol. 2, pp. 246–252, Feb. 1999.Google Scholar
  2. 2.
    Fatih Porikli, Yuri Ivanov and Tetsuji Haga ―“Robust Abandoned Object Detection Using Dual Foregrounds”,‖ EURASIP Journal on Advances in Signal Processing, 2008.Google Scholar
  3. 3.
    Y. Tian, M. Lu and A. Hampapur, ―“Robust and efficient foreground analysis for real-time video surveillance”,‖ in Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1182–1187, June 2005.Google Scholar
  4. 4.
    Xuli Li, Chao Zhang, Duo Zhang “Abandoned Objects Detection Using Double Illumination Invariant Foreground Masks” in Proc. 20th International Conference on Pattern Recognition, pp. 436–439, Aug. 2010.Google Scholar
  5. 5.
    Smith, K., Quelhas, P. and Gatica-Perez, D, ―“Detecting Abandoned Luggage Items in a Public Space”,‖ in Proc. Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 75–82, June 2006.Google Scholar
  6. 6.
    Abhineet Kumar Singh and Anupam Agarwal, “An Interactive Framework for Abandoned and Removed Object Detection in Video”, IEEE Indian Conference (INDICON),, 978-1-4799-2275-8/13, 2013.
  7. 7.
    K Jianting, Wen,Haifeng Gong, Xia Zhang and Wenze Hu Generative model for abandoned object detection.‖ IEEE International Conference on Image Processing, pp. 853–856, Nov. 2009.Google Scholar
  8. 8.
    F. Chang, C. Chen and C. Lu, “A linear-time component labeling algorithm using contour tracing technique” Computer Vision and Image Understanding, Vol. 93, Issue 2, pp. 206–220, Feb. 2004.Google Scholar
  9. 9.
    N. Bird, S. Atev, N. Caramelli, R. Martin, O. Masoud and N. Papanikolopoulos, -“Real Time, Online Detection of Abandoned Objects in Public Areas” in Proc. IEEE International Conference on Robotics and Automation, pp. 3775–3780, May 2006.Google Scholar
  10. 10.
    2007 IEEE International Conference on Advanced Video and Signal basedSurveillance., June 2013.
  11. 11.
    S. Winkler, C. J. van den Branden Lambrecht, and M. Kunt, “Vision and Video: Models and Applications”. In Christian J. van den Branden Lambrecht. Vision models and applications to image and video processing. Springer. pp. 209. ISBN 978-0-7923-7422-0, 2001.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Nagaraj Bhat
    • 1
  • U. Eranna
    • 2
  • B. M. Mahendra
    • 1
  • Savita Sonali
    • 3
  • Adokshaja Kulkarni
    • 4
  • Vikhyath Rai
    • 1
  1. 1.Department of ECERV College of EngineeringBengaluruIndia
  2. 2.Department of ECEBITM BellaryBellaryIndia
  3. 3.Department of ECERYMCOEBellaryIndia
  4. 4.Department of ECETCEGadagIndia

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