Advertisement

Optimal Camera Placement for Multimodal Video Summarization

  • Vishal ParikhEmail author
  • Priyanka Sharma
  • Vedang Shah
  • Vijay Ukani
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 958)

Abstract

Video Surveillance systems are used to monitor, observe and intercept the changes in activities, features and behavior of objects, people or places. A multimodal surveillance system incorporates a network of video cameras, acoustic sensors, pressure sensors, IR sensors and thermal sensors to capture the features of the entity under surveillance, and send the recorded data to a base station for further processing. Multimodal surveillance systems are utilized to capture the required features and use them for pattern recognition, object identification, traffic management, object tracking, and so on. The proposal is to develop an efficient camera placement algorithm for deciding placement of multiple video cameras at junctions and intersections in a multimodal surveillance system which will be capable of providing maximum coverage of the area under surveillance, which will leads to complete elimination or reduction of blind zones in a surveillance area, maximizing the view of subjects, and minimizing occlusions in high vehicular traffic areas. Furthermore, the proposal is to develop a video summarization algorithm which can be used to create summaries of the videos captured in a multi-view surveillance system. Such a video summarization algorithm can be used further for object detection, motion tracking, traffic segmentation, etc. in a multi-view surveillance system.

Keywords

Multimodal surveillance Multiview video summarization Camera placement 

References

  1. 1.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 3(1), 3 (2007)CrossRefGoogle Scholar
  2. 2.
    Money, A.G., Agius, H.: Video summarisation: a conceptual framework and survey of the state of the art. J. Vis. Commun. Image Represent. 19(2), 121–143 (2008)CrossRefGoogle Scholar
  3. 3.
    Panda, R., Roy-Chowdhury, A.K.: Multi-view surveillance video summarization via joint embedding and sparse optimization. IEEE Trans. Multimed. 19, 2010–2021 (2017)CrossRefGoogle Scholar
  4. 4.
    Zouaoui, R., et al.: Embedded security system for multi-modal surveillance in a railway carriage. In: Proceedings of SPIE, January 2016Google Scholar
  5. 5.
    Wang, T., Zhu, Z.: Multimodal and multi-task audio-visual vehicle detection and classification. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 440–446, September 2012Google Scholar
  6. 6.
    Magno, M., Tombari, F., Brunelli, D., Stefano, L.D., Benini, L.: Multimodal abandoned/removed object detection for low power video surveillance systems. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 188–193, September 2009Google Scholar
  7. 7.
    Gupta, H., Yu, L., Hakeem, A., Choe, T.E., Haering, N.: Multimodal complex event detection framework for wide area surveillance. In: CVPR 2011 Workshops, pp. 47–54, June 2011Google Scholar
  8. 8.
    Prati, A., Vezzani, R., Benini, L., Farella, E., Zappi, P.: An integrated multi-modal sensor network for video surveillance. In: Proceedings of the Third ACM International Workshop on Video Surveillance & Sensor Networks, pp. 95–102 (2005)Google Scholar
  9. 9.
    Rios-Cabrera, R., Tuytelaars, T., Gool, L.V.: Efficient multi-camera vehicle detection, tracking, and identification in a tunnel surveillance application. Comput. Vis. Image Underst. 116, 742–753 (2012)CrossRefGoogle Scholar
  10. 10.
    Lopatka, K., Kotus, J., Szczodrak, M., Marcinkowski, P., Korzeniewski, A., Czyzewski, A.: Multimodal audio-visual recognition of traffic events. In: 2011 22nd International Workshop on Database and Expert Systems Applications, pp. 376–380, August 2011Google Scholar
  11. 11.
    Wang, Y.K., Fan, C.T., Huang, C.R.: A large scale video surveillance system with heterogeneous information fusion and visualization for wide area monitoring. In: 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 178–181, July 2012Google Scholar
  12. 12.
    van den Hengel, A., et al.: Automatic camera placement for large scale surveillance networks. In: 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–6, December 2009Google Scholar
  13. 13.
    Yildiz, E., Akkaya, K., Sisikoglu, E., Sir, M.Y.: Optimal camera placement for providing angular coverage in wireless video sensor networks. IEEE Trans. Comput. 63, 1812–1825 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhao, J., Cheung, S.C., Nguyen, T.: Optimal camera network configurations for visual tagging. IEEE J. Sel. Top. Signal Process. 2, 464–479 (2008)CrossRefGoogle Scholar
  15. 15.
    Liu, L., Xing, J., Ai, H.: Multi-view vehicle detection and tracking in crossroads. In: The First Asian Conference on Pattern Recognition, pp. 608–612, November 2011Google Scholar
  16. 16.
    Denman, S., et al.: Multi-view intelligent vehicle surveillance system. In: 2006 IEEE International Conference on Video and Signal Based Surveillance, p. 26, November 2006Google Scholar
  17. 17.
    Wang, K., Liu, Y., Gou, C., Wang, F.Y.: A multi-view learning approach to foreground detection for traffic surveillance applications. IEEE Trans. Veh. Technol. 65, 4144–4158 (2016)CrossRefGoogle Scholar
  18. 18.
    Zheng, R., Yao, C., Jin, H., Zhu, L., Zhang, Q., Deng, W.: Parallel key frame extraction for surveillance video service in a smart city, vol. 10, pp. 1–8, August 2015CrossRefGoogle Scholar
  19. 19.
    Panda, R., Dasy, A., Roy-Chowdhury, A.K.: Video summarization in a multi-view camera network. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2971–2976, December 2016Google Scholar
  20. 20.
    Kuanar, S.K., Ranga, K.B., Chowdhury, A.S.: Multi-view video summarization using bipartite matching constrained optimum-path forest clustering. IEEE Trans. Multimed. 17, 1166–1173 (2015)CrossRefGoogle Scholar
  21. 21.
    Liu, S., Lai, S.: Schematic visualization of object trajectories across multiple cameras for indoor surveillances. In: 2009 Fifth International Conference on Image and Graphics, pp. 406–411, September 2009Google Scholar
  22. 22.
    Krause, J., Stark, M., Deng, J., Fei-Fei, L.: The ko-per intersection laserscanner and video dataset. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1900–1901, October 2014Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vishal Parikh
    • 1
    Email author
  • Priyanka Sharma
    • 1
  • Vedang Shah
    • 1
  • Vijay Ukani
    • 1
  1. 1.Institute of TechnologyNirma UniversityAhmedabadIndia

Personalised recommendations