Detección y Seguimiento de Personas Basado en Estereovisión y Filtro de Kalman

  1. Jorge García 1
  2. Alfredo Gardel 1
  3. Ignacio Bravo 1
  4. José Luis Lázaro 1
  5. Miguel Martínez 1
  1. 1 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2012

Volume: 9

Issue: 4

Pages: 453-461

Type: Article

DOI: 10.1016/J.RIAI.2012.09.012 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

The people counting systems are widely used in surveillance applications. This article presents an application for counting people through a stereovision system. This system obtains counting rates of people moving through the counting area, distinguishing between input and output. To achieve this aim is required two basic steps: detection and tracking. The detection step is based on correlation through a pre-processed image with various circular patterns in order to search people's heads, filtering these detections by stereovision depending on the height. The people tracking is carried out through a multiple hypothesis algorithm based on the Kalman filter. Finally, people counting is done according to the trajectory followed by the person. To validate the algorithm have been used several real videos taken from different transit areas inside buildings, reaching rates ranging between 87% and 98% accuracy depending on the number of people crossing the counting zone simultaneously. In these videos occur several adverse situations, such as occlusions, people in groups in different directions, lighting changes, etc.

Bibliographic References

  • Barandiaran, J., Murguia, B., Boto, F., 2008. Real-time people counting using multiple lines. In: Proc. Ninth Int. Workshop Image Analysis for Multimedia Interactive Services WIAMIS ’08. pp. 159–162.
  • Chan, A. B., Liang, Z.-S. J., Vasconcelos, N., 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition CVPR 2008. pp. 1–7.
  • Chen, T.-H., Chen, T.-Y., Chen, Z.-X., 2006. An intelligent people-flow counting method for passing through a gate. In: Proc. IEEE Conf. Robotics, Automation and Mechatronics. pp. 1–6.
  • Donate, A., Liu, X., Collins, E. G., 2011. Efficient path-based stereo matching with subpixel accuracy. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41 (1), 183–195.
  • Englebienne, G., van Oosterhout, T., Krose, B., 2009. Tracking in sparse multicamera setups using stereo vision. In: Proc. Third ACM/IEEE Int. Conf. Distributed Smart Cameras ICDSC 2009. pp. 1–6.
  • Hassan, M., Khalid, N. E. A., Ibrahim, A., Noor, N. M., 2008. Evaluation of sobel, canny, shen & castan using sample line histogram method. In: Proc. Int. Symp. Information Technology ITSim 2008. Vol. 3. pp. 1–7.
  • Lee, J. H., Kim, Y.-S., Kim, B. K., Ohba, K., Kawata, H., Ohya, A., Yuta, S., 2007. Security door system using human tracking method with laser range finders. In: Proc. Int. Conf. Mechatronics and Automation ICMA 2007. pp. 2060–2065.
  • Mucientes, M., Burgard, W., oct. 2006. Multiple hypothesis tracking of clusters of people. In: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on. pp. 692 –697.
  • Patil, R., Rybski, P. E., Kanade, T., Veloso, M. M., 2004. People detection and tracking in high resolution panoramic video mosaic. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS 2004). Vol. 2. pp. 1323–1328.
  • Rigoll, G., Eickeler, S., Muller, S., 2000. Person tracking in real-world scenarios using statistical methods. In: Proc. Fourth IEEE Int Automatic Face and Gesture Recognition Conf. pp. 342–347.
  • Rizzon, L., Massari, N., Gottardi, M., Gasparini, L., 2009. A low-power people counting system based on a vision sensor working on contrast. In: Proc. IEEE Int. Symp. Circuits and Systems ISCAS 2009.
  • Shaik, Z., Asari, V., 2007. A robust method for multiple face tracking using kalman filter. In: Proc. 36th IEEE Applied Imagery Pattern Recognition Workshop AIPR 2007. pp. 125–130.
  • Velipasalar, S., Tian, Y.-L., Hampapur, A., 2006. Automatic counting of interacting people by using a single uncalibrated camera. In: Proc. IEEE Int Multimedia and Expo Conf. pp. 1265–1268.
  • Xu, H., Lv, P., Meng, L., 2010. A people counting system based on headshoulder detection and tracking in surveillance video. In: Proc. Int Computer Design and Applications (ICCDA) Conf. Vol. 1.
  • Yu, S., Chen, X., Sun, W., Xie, D., 2008. A robust method for detecting and counting people. In: Proc. Int. Conf. Audio, Language and Image Processing ICALIP 2008. pp. 1545–1549.
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