Motion Tracking of Humans under Occlusion Using Blobs

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

In today’s scenario, Video Surveillance plays a major role in building intelligent systems. This involves the phases such as Motion detection, Object classification and Object tracking. Among these, Object tracking is an important task to identify/detect the objects and track its motion correspondingly. After object identification, the location of the objects is crucial to understand the nature of the moving objects. There arises a need for tracking the occluded objects also when multiple objects are under surveillance. In this paper, a new tracking mechanism has been proposed to track the objects under surveillance though they occlude. Initially, background has been modelled with the Adaptive background modelling using GMM (Gaussian Mixture Model) to obtain the foreground as blobs. Later, Objects represented using Contours are integrated with simple particle filters to obtain a new state which would track the object/person effectively. Using the path estimated by particle filters, Occlusion of blobs gets determined based on the interference of their radii lying in that path. Performance of this system has been tested over CAVIAR and User generated data sets and results seem to be promising.

Keywords

Video Surveillance Object Tracking Background Modelling Blob Tracking Particle Filters 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computing Surveys 38(4) (2006)Google Scholar
  2. 2.
    Jahandide, H., Pour, K.M., Moghaddam, H.A.: A Hybrid Motion and Appearance prediction model for Robust Visual Object Tracking. Pattern Recognition Letter 33(16), 2192–2197 (2012)CrossRefGoogle Scholar
  3. 3.
    Bhaskar, H., Maskell, L.M.S.: Articulated Human body parts detection based on cluster background subtraction and foreground matching. Neurocomputing 100, 58–73 (2013)CrossRefGoogle Scholar
  4. 4.
    Manjunath, G.D., Abirami, S.: Suspicious Human activity detection from Surveillance videos. International Journal on Internet and Distributed Computing Systems 2(2), 141–149 (2012)Google Scholar
  5. 5.
    Gowshikaa, D., Abirami, S., Baskaran, R.: Automated Human Behaviour Analysis from Surveillance videos: a survey. Artificial Intelligence Review (April 2012), doi:10.1007/s 10462-012-9341-3Google Scholar
  6. 6.
    Stauffer, C., Grimson, E.E.L.: Learning patterns of activity using real-time tracking. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  7. 7.
    Huwer, S., Niemann, H.: Adaptive Change Detection for Real-time Surveillance applications. In: the Proceedings of 3rd IEEE Workshop on Visual Surveillance, pp. 37–45 (2000)Google Scholar
  8. 8.
    Haibo, H., Hong, Z.: Real-time Tracking in Image Sequences based-on Parameters Updating with Temporal and Spatial Neighbourhoods Mixture Gaussian Model. Proceedings of World Academy of Science, Engineering and Technology, 754–759 (2010)Google Scholar
  9. 9.
    Lu, J.-G., Cai, A.-N.: Tracking people through partial occlusions. The Journal of China Universities of Post and Telecommunications 16(2), 117–121 (2009)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Cho, N.G., Yuille, A.L., Lee, S.W.: Adaptive Occlusion State estimation for human pose tracking under self-occlusions. Pattern Recognition 46(3) (2013)Google Scholar
  11. 11.
    Varcheie, P.D.Z., Sills-Lavoie, M., Bilodeau, G.-A.: A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm. The Proceedings of Sensor Journal 10(2), 1041–1061 (2010)CrossRefGoogle Scholar
  12. 12.
    Fradi, H., Dugelay, J.-L.: Robust Foreground Segmentation using Improved Gaussian Mixture Model and Optical flow. In: The Proceedings of International Conference on Informatics, Electronics and Vision, pp. 248–253 (2012)Google Scholar
  13. 13.
    Wang, Y., Tang, X., Cui, Q.: Dynamic Appearance model for particle filter based visual tracking. Pattern Recognition 45(12), 4510–4523 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Science and Technology, College of EngineeringAnna UniversityChennaiIndia

Personalised recommendations