Multi-perspective Video Analysis of Persons and Vehicles for Enhanced Situational Awareness

  • Sangho Park
  • Mohan M. Trivedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


This paper presents a multi-perspective vision-based analysis of people and vehicle activities for the enhancement of situational awareness. Multiple perspectives provide a useful invariant feature of object in image, i.e., the footage area on the ground. Moving objects are detected in image domain, and tracking results of the objects are represented in projection domain using planar homography. Spatio-temporal relationships between human and vehicle tracks are categorized to safe or unsafe situation depending on site context such as walkway and driveway locations. Semantic-level information of the situation is achieved with the anticipation of possible directions of near-future tracks using piecewise velocity history. Crowd density is estimated from the footage in homography plane. Experimental data show promising results. Our framework can be applied to broad range of situational awareness for emergency response, disaster prevention, human interactions in structured environments, and crowd movement analysis in wide-view areas.


Gaussian Mixture Model Situational Awareness Virtual View World Coordinate System Foreground Region 
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.
    Aggarwal, J.K., Cai, Q.: Human motion analysis: a review. Computer Vision and Image Understanding 73(3), 295–304 (1999)CrossRefGoogle Scholar
  2. 2.
    Antonini, G., Bierlaire, M.: Capturing interactions in pedestrian walking behavior in a discrete choice framework. Transportation Research Part B (September 2005)Google Scholar
  3. 3.
    Gavrila, D.: The visual analysis of human movement: a survey. Computer Vision and Image Understanding 73(1), 82–98 (1999)MATHCrossRefGoogle Scholar
  4. 4.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time surveillance of people and their activities. IEEE transactions on Pattern Analysis and Machine Intelligence 22(8), 797–808 (2000)CrossRefGoogle Scholar
  5. 5.
    McKenna, S.J., Jabri, S., Duric, Z., Wechsler, H.: Tracking interacting people. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), pp. 348–353 (2000)Google Scholar
  6. 6.
    Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 831–843 (2000)CrossRefGoogle Scholar
  7. 7.
    Park, S., Trivedi, M.M.: A track-based human movement analysis and privacy protection system adaptive to environmental contexts. In: IEEE International Conference on Advanced Video and Signal based Surveillance, Como, Italy (2005)Google Scholar
  8. 8.
    Remagnino, P., Shihab, A.I., Jones, G.A.: Distributed intelligence for multi-camera visual surveillance. Pattern Recognition: Special Issue on Agent-based Computer Vision 37(4), 675–689 (2004)Google Scholar
  9. 9.
    Trivedi, M.M., Gandhi, T., Huang, K.: Distributed interactive video arrays for event capture and enhanced situational awareness. In: IEEE Intelligent Systems, Special Issue on Artificial Intelligence for Homeland Security (September 2005)Google Scholar
  10. 10.
    Velastin, S.A., Boghossian, B.A., Lo, B., Sun, J., Vicencio-Silva, M.A.: Prismatica: Toward ambient intelligence in public transport environments. IEEE Transactions on Systems, Man, and Cybernetics -Part A 35(1), 164–182 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sangho Park
    • 1
  • Mohan M. Trivedi
    • 1
  1. 1.Computer Vision and Robotics Research LaboratoryUniversity of California at San DiegoLa JollaUSA

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