Depth Map’s 2D Histogram Assisted Occlusion Handling in Video Object Tracking

  • Adam Łuczak
  • Sławomir Maćkowiak
  • Jakub Siast
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8671)


The paper describes new algorithm for automatic video object tracking. Proposed architecture consists of two loops of Kalman filter. In the loop of the tracking process, the information achieved from video and from 2D histogram based on depth map is used. Two loops work simultanously and the parameters between the loops are interchanged when the occlusion occurs. The 2D histogram representation of the depth map has unique properties that can be used to improve the tracking eficiency especially in the case of occlusions of the objects in the image. Experimental results prove that the proposed system can accurately track multiple objects in complex scenes.


Tracking Algorithm Object Tracking Kalman Gain Motion Object Segmentation Stereo Camera System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Pulford, G.W.: Taxonomy of multiple target tracking methods. IEE Proceedings Radar, Sonar and Navigation 152(5), 291–304 (2005)CrossRefGoogle Scholar
  2. 2.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys (CSUR) 38(4), 1–45 (2006)CrossRefGoogle Scholar
  3. 3.
    Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities - a survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)CrossRefGoogle Scholar
  4. 4.
    Du, Y., Chen, F., Xu, W., Li, Y.: Recognizing interaction activities using dynamic Bayesian network. In: International Conference on Pattern Recogntion, pp. 618–621 (2006)Google Scholar
  5. 5.
    Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page, (online December 1, 2013)
  6. 6.
    Yongtae, K., Jiyoung, K., Wanghoon, S.-K.-H.: Fast Disparity and Motion Estimation for Multi-view Video Coding. IEEE Transactions on Consumer Electronics 53(2), 712–719 (2007)CrossRefGoogle Scholar
  7. 7.
    Wu, D., Zhang, H., Li, X., Qian, L.: Depth Map Generation Algorithm for Multiview Video. In: 2013 Fifth International Conference on Computational and Information Sciences (ICCIS), Shiyang, China (2013)Google Scholar
  8. 8.
    Lee, L.S.H., Sharma, S.: Real-time disparity estimation algorithm for stereo camera systems. IEEE Transactions on Consumer Electronics 57(3), 1018–1026 (2011)CrossRefGoogle Scholar
  9. 9.
    Han, H., Han, X., Yang, F.: An improved gradient-based dense stereo correspondence algorithm using guided filter. International Journal for Light and Electron Optics 125(1), 115–120 (accepted for publication, 2014)Google Scholar
  10. 10.
    Claps, A., Reyes, M., Escalera, S.: Multi-modal user identification and object recognition surveillance system. Pattern Recognition Letters 34(7), 799–808 (2013) ISSN 0167-8655Google Scholar
  11. 11.
    Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page, (online December 1, 2013)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Adam Łuczak
    • 1
  • Sławomir Maćkowiak
    • 1
  • Jakub Siast
    • 1
  1. 1.Poznań University of TechnologyPoland

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