An Improved Real-time Method for Counting People in Crowded Scenes Based on a Statistical Approach

Shirine Riachi, Walid Karam, Hanna Greige

2014

Abstract

In this paper, we present a real-time method for counting people in crowded conditions using an indirect/statistical approach. Our method is based on an algorithm by Albiol et al. that won the PETS 2009 contest on people counting. We employ a scale-invariant interest point detector from the state of the art coined SURF (Speeded-Up Robust Features), and we exploit motion information to retain only interest points belonging to moving people. Direct proportionality is then assumed between the number of remaining SURF points and the number of people. Our technique was first tested on three video sequences from the PETS dataset. Results showed an improvement over Albiol’s in all the three cases. It was then tested on our set of video sequences taken under various conditions. Despite the complexity of the scenes, results were very reasonable with a mean relative error ranging from 9.36% to 17.06% and a mean absolute error ranging from 1.13 to 3.33. Testing this method on a new dataset proved its speed and accuracy under many shooting scenarios, especially in crowded conditions where the averaging process reduces the variations in the number of detected SURF points per person.

References

  1. Albiol, A., Silla, M. J., Albiol, A., & Mossi, J. E. M. (2009). Video analysis using corner motion statistics. In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (pp. 31-38).
  2. Barjatya, A. (2004). Block matching algorithms for motion estimation. IEEE Transactions Evolution Computation, 8(3), 225-239.
  3. Bauer, J., Sunderhauf, N., & Protzel, P. (2007). Comparing several implementations of two recently published feature detectors. In Proceedings of the International Conference on Intelligent and Autonomous Systems (Vol. 6, No. pt 1).
  4. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
  5. Chan, A. B., Liang, Z. S., & Vasconcelos, N. (2008). Privacy preserving crowd monitoring: Counting people without people models or tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1-7).
  6. Conte, D., Foggia, P., Percannella, G., Tufano, F., & Vento, M. (2010). A method for counting people in crowded scenes. In Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 225-232).
  7. Davies, A. C., Yin, J. H., & Velastin, S. A. (1995). Crowd monitoring using image processing. Electronics & Communication Engineering Journal, 7(1), 37-47.
  8. Fradi, H., & Dugelay, J. (2012). People counting system in crowded scenes based on feature regression. In Proceedings of the 20th European Signal Processing Conference (Eusipco) (pp. 136-140).
  9. Huang, C. L., Hsu, S. C., Tsao, I. C., Huang, B. S., Wang, H. W., & Lin, H. W. (2011). People counting using ellipse detection and forward/backward tracing. In First Asian Conference on Pattern Recognition (ACPR) (pp. 505-509).
  10. Li, J., Huang, L., & Liu, C. (2011, August). Robust people counting in video surveillance: Dataset and system. In 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS) (pp. 54- 59).
  11. Li, M., Zhang, Z., Huang, K., & Tan, T. (2008). Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In 19th International Conference on Pattern Recognition (ICPR) (pp. 1-4).
  12. Merad, D., Aziz, K. E., & Thome, N. (2010). Fast people counting using head detection from skeleton graph. In Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 233- 240).
  13. Ma, R., Li, L., Huang, W., & Tian, Q. (2004). On pixel count based crowd density estimation for visual surveillance. In IEEE Conference on Cybernetics and Intelligent Systems (Vol. 1, pp. 170-173).
  14. Marana, A. N., Velastin, S. A., Costa, L. D. F., & Lotufo, R. A. (1998). Automatic estimation of crowd density using texture. Safety Science, 28(3), 165-175.
  15. Nie, Y., & Ma, K. K. (2002). Adaptive rood pattern search for fast block-matching motion estimation. IEEE Transactions on Image Processing, 11(12), 1442- 1449.
  16. PETS dataset. (n.d.). Retrieved April 14, 2013 from http://www.cvg.rdg.ac.uk/PETS2013/a.html.
  17. Rahmalan, H., Nixon, M. S., & Carter, J. N. (2006). On crowd density estimation for surveillance. In The Institution of Engineering and Technology Conference on Crime and Security (pp. 540-545).
  18. Subburaman, V. B., Descamps, A., & Carincotte, C. (2012). Counting people in the crowd using a generic head detector. In IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS) (pp. 470-475).
  19. Wen, Q., Jia, C., Yu, Y., Chen, G., Yu, Z., & Zhou, C. (2011). People number estimation in the crowded scenes using texture analysis based on gabor filter. Journal of Computational Information Systems, 7(11), 3754-3763.
  20. Zeng, C., & Ma, H. (2010). Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In 20th International Conference on Pattern Recognition (ICPR) (pp. 2069-2072).
  21. Zhang, E., & Chen, F. (2007). A fast and robust people counting method in video surveillance. In International Conference on Computational Intelligence and Security (pp. 339-343).
  22. Zhao, X., Delleandrea, E., & Chen, L. (2009). A people counting system based on face detection and tracking in a video. In Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 67-72).
Download


Paper Citation


in Harvard Style

Riachi S., Karam W. and Greige H. (2014). An Improved Real-time Method for Counting People in Crowded Scenes Based on a Statistical Approach . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 203-212. DOI: 10.5220/0005108002030212


in Bibtex Style

@conference{icinco14,
author={Shirine Riachi and Walid Karam and Hanna Greige},
title={An Improved Real-time Method for Counting People in Crowded Scenes Based on a Statistical Approach},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={203-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005108002030212},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - An Improved Real-time Method for Counting People in Crowded Scenes Based on a Statistical Approach
SN - 978-989-758-040-6
AU - Riachi S.
AU - Karam W.
AU - Greige H.
PY - 2014
SP - 203
EP - 212
DO - 10.5220/0005108002030212