Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry

Pranav Mantini, Shishir K. Shah

Abstract

This paper proposes an algorithm to optimize the placement of surveillance cameras in a 3D infrastructure. The key differentiating feature in the algorithm design is the incorporation of human behavior within the infrastructure for optimization. Infrastructures depending on their geometries may exhibit regions with dominant human activity. In the absence of observations, this paper presents a method to predict this human behavior and identify such regions to deploy an effective surveillance scenario. Domain knowledge regarding the infrastructure was used to predict the possible human motion trajectories in the infrastructure. These trajectories were used to identify areas with dominant human activity. Furthermore, a metric that quantifies the position and orientation of a camera based on the observable space, activity in the space, pose of objects of interest within the activity, and their image resolution in camera view was defined for optimization. This method was compared with the state-of-the-art algorithms and the results are shown with respect to amount of observable space, human activity, and face detection rate per camera in a configuration of cameras.

References

  1. Bodor, R., Drenner, A., Schrater, P., and Papanikolopoulos, N. (2007). Optimal camera placement for automated surveillance tasks. Journal of Intelligent and Robotic Systems, 50(3):257-295.
  2. Chen, X. and Davis, J. (2000). Camera placement considering occlusion for robust motion capture. Technical report.
  3. Debaque, B., Jedidi, R., and Prevost, D. (2009). Optimal video camera network deployment to support security monitoring. In Information Fusion, 2009. FUSION 7809. 12th International Conference on, pages 1730- 1736.
  4. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1-38.
  5. Erdem, U. M. and Sclaroff, S. (2004). Optimal placement of cameras in floorplans to satisfy task requirements and cost constraints. In In Proc. of OMNIVIS Workshop.
  6. Filho, C., de Oliveira, A., and Costa, M. (2010). Using random restart hill climbing algorithm for minimization of component assembly time printed circuit boards. Latin America Transactions, IEEE (Revista IEEE America Latina), 8(1):23-29.
  7. Fisk, S. (1978). A short proof of Chvtal's Watchman Theorem. Journal of Combinatorial Theory, 24.
  8. Fleishman, S., Cohen-Or, D., and Lischinski, D. (1999). Automatic camera placement for image-based modeling. In Computer Graphics and Applications, 1999. Proceedings. Seventh Pacific Conference on , pages 12-20, 315.
  9. Hörster, E. and Lienhart, R. (2006a). Approximating optimal visual sensor placement. In Multimedia and Expo, 2006 IEEE International Conference on, pages 1257- 1260.
  10. Hörster, E. and Lienhart, R. (2006b). Calibrating and optimizing poses of visual sensors in distributed platforms. Multimedia Systems, 12(3):195-210.
  11. Hörster, E. and Lienhart, R. (2006c). On the optimal placement of multiple visual sensors. In Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, VSSN 7806, pages 111- 120, New York, NY, USA. ACM.
  12. Huang, H., Ni, C.-C., Ban, X., Gao, J., Schneider, A., and Lin, S. (2014). Connected wireless camera network deployment with visibility coverage. In INFOCOM, 2014 Proceedings IEEE.
  13. Janoos, F., Machiraju, R., Parent, R., Davis, J. W., and Murray, A. (2007). Sensor configuration for coverage optimization for surveillance applications. In SPIE, 2007 Proceedings.
  14. Kim, K. and Murray, A. T. (2008). Enhancing spatial representation in primary and secondary coverage location modeling*. Journal of Regional Science, 48(4):745- 768.
  15. Kitani, K., Ziebart, B., Bagnell, J., and Hebert, M. (2012). Activity forecasting. Lecture Notes in Computer Science, pages 201-214.
  16. Lienhart, R. and Maydt, J. (2002). An extended set of haarlike features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I-900-I-903 vol.1.
  17. Malik, R. and Bajcsy, P. (2008). Automated Placement of Multiple Stereo Cameras. In The 8th Workshop on Omnidirectional Vision, Camera Networks and Nonclassical Cameras - OMNIVIS, Marseille, France. Rahul Swaminathan and Vincenzo Caglioti and Antonis Argyros.
  18. Mantini, P. and Shah, S. (2014). Human trajectory forecasting in indoor environments using geometric context. In Proceedings of the Ninth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 7814. ACM.
  19. Mittal, A. and Davis, L. (2004). Visibility analysis and sensor planning in dynamic environments. In Pajdla, T. and Matas, J., editors, Computer Vision - ECCV 2004, volume 3021 of Lecture Notes in Computer Science, pages 175-189. Springer Berlin Heidelberg.
  20. Murray, A. T., Kim, K., Davis, J. W., Machiraju, R., and Parent, R. (2007). Coverage optimization to support security monitoring. Computers, Environment and Urban Systems, 31(2):133 - 147.
  21. Ram, S., Ramakrishnan, K. R., Atrey, P. K., Singh, V. K., and Kankanhalli, M. S. (2006). A design methodology for selection and placement of sensors in multimedia surveillance systems. In Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, VSSN 7806, pages 121- 130, New York, NY, USA. ACM.
  22. Sivaram, G. S. V. S., Kankanhalli, M. S., and Ramakrishnan, K. R. (2009). Design of multimedia surveillance systems. ACM Trans. Multimedia Comput. Commun. Appl., 5(3):23:1-23:25.
  23. Tarabanis, K., Allen, P., and Tsai, R. (1995). A survey of sensor planning in computer vision. Robotics and Automation, IEEE Transactions on, 11(1):86-104.
  24. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I-511-I-518 vol.1.
  25. Yabuta, K. and Kitazawa, H. (2008). Optimum camera placement considering camera specification for security monitoring. In Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on, pages 2114- 2117.
  26. Zhang, Y., Lei, T., Barzilay, R., and Jaakkola, T. (2014). Greed is Good if Randomized: New Inference for Dependency Parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics.
  27. Zhou, W., Xiong, H., Ge, Y., Yu, J., Ozdemir, H., and Lee, K. (2010). Direction clustering for characterizing movement patterns. In Information Reuse and Integration (IRI), 2010 IEEE International Conference on, pages 165-170.
Download


Paper Citation


in Harvard Style

Mantini P. and Shah S. (2016). Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 225-235. DOI: 10.5220/0005677602250235


in Bibtex Style

@conference{visapp16,
author={Pranav Mantini and Shishir K. Shah},
title={Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={225-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005677602250235},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry
SN - 978-989-758-175-5
AU - Mantini P.
AU - Shah S.
PY - 2016
SP - 225
EP - 235
DO - 10.5220/0005677602250235