Interactive Motion Analysis for Video Surveillance and Long Term Scene Monitoring

  • Andrew W. Senior
  • YingLi Tian
  • Max Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)


In video surveillance and long term scene monitoring applications, it is a challenging problem to handle slow-moving or stopped objects for motion analysis and tracking. We present a new framework by using two feedback mechanisms which allow interactions between tracking and background subtraction (BGS) to improve tracking accuracy, particularly in the cases of slow-moving and stopped objects. A publish-subscribe modular system that provides the framework for communication between components is described. The robustness and efficiency of the proposed method is tested on our real time video surveillance system. Quantitative performance evaluation is performed on a variety of sequences, including standard datasets. With the two feedback mechanisms enabled together, significant improvement in tracking performance are demonstrated particularly in handling slow moving and stopped objects.


video surveillance slow-moving and stopped object tracking motion analysis interaction background subtraction 


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  1. 1.
    Connell, J., Senior, A.W., Hampapur, A., Tian, Y.-L., Brown, L., Pankanti, S.: Detection and tracking in the IBM People-Vision system. In: IEEE ICME (June 2004)Google Scholar
  2. 2.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1999)Google Scholar
  3. 3.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Proc. IEEE International Conference on Computer Vision, vol. 1 (1999)Google Scholar
  4. 4.
    Boult, T., Micheals, R.J., Gao, X., Eckmann, M.: Into the woods: Visual surveillance of non-cooperative and camouflaged targets in compex outdoor settings. Proceedings of the IEEE 89(10), 1382–1402 (2001)CrossRefGoogle Scholar
  5. 5.
    Tian, Y., Lu, M., Hampapur, A.: Robust and Efficien foreground Analysis for Real-time Video Surveillance. In: Computer Vision and Pattern Recognition (2005)Google Scholar
  6. 6.
    Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: IEEE Workshop on Motion and Video Computing (2002)Google Scholar
  7. 7.
    Abbott, R., Williams, L.: Multiple target tracking with lazy background subtraction and connected components analysis. Tech. Rep., University of New Mexico (June 2005)Google Scholar
  8. 8.
    Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 543–560. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Pnevmatikakis, A., Polymenakos, L.: Kalman tracking with target feedback on adaptive background learning. In: Workshop on Multimodal Interaction and Related Machine Learning Algorithms (2006)Google Scholar
  10. 10.
    Cheung, S.-C., Kamath, C.: Robust background subtraction with foreground validation for urban traffic video. EURASIP Journal of Applied Signal Processing, Special Issue on Advances in Intelligent Vision Systems (2005)Google Scholar
  11. 11.
    Wang, J.X., Bebis, G.N., Miller, R.: Robust video-based surveillance by integrating target detection with tracking. In: Computer Vision and Pattern Recognition (2006)Google Scholar
  12. 12.
    Senior, A.: Tracking with probabilistic appearance models. In: Third International Workshop on Performance Evaluation of Tracking and Surveillance systems (June 2002)Google Scholar
  13. 13.
    Venetianer, P., Zhang, Z., Yin, W., Lipton, A.: Stationary target detection using the objectvideo surveillance system. Advanced Video and Signal-based Surveillance (2007)Google Scholar
  14. 14.
    Yao, J., Odobez, J.-M.: Multi-layer background subtraction based on color and texture. In: Proc. IEEE Conference on Visual Surveillance (2007)Google Scholar
  15. 15.
    Taycher, L., Fisher III, J.W., Darrell, T.: Incorporating object tracking feedback into background maintenance framework. In: IEEE Workshop on Motion and Video Computing (2005)Google Scholar
  16. 16.
    PETS 2001 Benchmark Data (2001),
  17. 17.
    Collins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE 89(10) (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrew W. Senior
    • 1
  • YingLi Tian
    • 2
  • Max Lu
    • 3
  1. 1.Google ResearchNew YorkUSA
  2. 2.Department of Electrical EngineeringThe City College, City University of New YorkNew YorkUSA
  3. 3.IBM Global Technology ServicesHawthorneUSA

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