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Machine Vision and Applications

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01-02-2016 | Short Paper

Counting pedestrians with a zenithal arrangement of depth cameras

Authors: Pablo Vera, Sergio Monjaraz, Joaquín Salas

Published in: Machine Vision and Applications | Issue 2/2016

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Abstract

Counting people is a basic operation in applications that include surveillance, marketing, services, and others. Recently, computer vision techniques have emerged as a non-intrusive, cost-effective, and reliable solution to the problem of counting pedestrians. In this article, we introduce a system capable of counting people using a cooperating network of depth cameras placed in zenithal position. In our method, we first detect people in each camera of the array separately. Then, we construct and consolidate tracklets based on their closeness and time stamp. Our experimental results show that the method permits to extend the narrow range of a single sensor to wider scenarios.

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Chan, A., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. IEEE Trans. Image Process. 21(4), 2160–2177 (2012)CrossRefMathSciNet

Chen, K., Kamarainen, J.-K.: Learning to count with back-propagated information. In: International Conference on Pattern Recognition, pp. 4672–4677. IEEE (2014)

Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)CrossRef

Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRef

Ferryman, J., Ellis, A.-L.: Performance evaluation of crowd image analysis using the PETS2009 dataset. Pattern Recognit. Lett. 44, 3–15 (2014)CrossRef

Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)CrossRefMathSciNet

Galc̆ík, F., Gargalík, R.: Real-time depth map based people counting. In: International Conference on Advanced Concepts for Intelligent Vision Systems, vol. 8192, p. 330. Springer (2013)

Gao, K.: An emergency evacuation model based on computer vision smart inducing in hotel stampede environment. In: Applied Mechanics and Materials, vol. 556, pp. 5736–5739. Trans Tech Publ (2014)

Golub, G., Van Loan, C.: Matrix Computations, vol. 3. JHU Press, Baltimore (2012)

10.

Herrera, C., Kannala, J., Heikkilä, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2058–2064 (2012)CrossRef

11.

Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: International Conference on Pattern Recognition, vol. 3, pp. 1187–1190. IEEE (2006)

12.

Kuhn, H.W.: The Hungarian Method for the Assignment Problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)CrossRef

13.

Lee, K., Eidson, J., Weibel, H., Mohl, D.: IEEE 1588-standard for a precision clock synchronization protocol for networked measurement and control systems. In: Conference on IEEE, vol. 1588, p. 2 (2005)

14.

Lemkens, W., Kaur, P., Buys, K., Slaets, P., Tuytelaars, T., De Schutter, J.: Multi RGB-D camera setup for generating large 3D point clouds. In: International Conference on Intelligent Robots and Systems, pp. 1092–1099. IEEE (2013)

15.

Macknojia, R., Chávez-Aragón, A., Payeur, P., Laganiere, R.: Calibration of a network of kinect sensors for robotic inspection over a large workspace. In: IEEE Workshop on Robot Vision, pp. 184–190. IEEE (2013)

16.

Maddalena, L., Petrosino, A., Russo, F.: People counting by learning their appearance in a multi-view camera environment. Pattern Recognit. Lett. 36, 125–134 (2014)CrossRef

17.

Maimone, A., Fuchs, H.: Reducing interference between multiple structured light depth sensors using motion. In: IEEE Virtual Reality, pp. 51–54. IEEE (2012)

18.

Martin Martin, R., Lorbach, M., Brock, O.: Deterioration of depth measurements due to interference of multiple RGB-D sensors. In: International Conference on Intelligent Robots and Systems, pp. 4205–4212. IEEE (2014)

19.

Mikhelson, I.V., Lee, P.G., Sahakian, A.V., Wu, Y., Katsaggelos, A.K.: Automatic, fast, online calibration between depth and color cameras. J. Vis. Commun. Image Rep. 25(1), 218–226 (2014)CrossRef

20.

Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18(12), 1163–1173 (1996)CrossRef

21.

Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Advances in Neural Information Processing Systems, pp. 424–432 (2014)

22.

Porzycki, J., Lubaś, R., Mycek, M., Wkas, J.: Dynamic data–driven simulation of pedestrian movement with automatic validation. In: Traffic and Granular Flow, pp. 129–136. Springer (2015)

23.

Rauter, M.: Reliable human detection and tracking in top-view depth images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 529–534 (2013)

24.

Ryan, D., Denman, S., Fookes, C., Sridharan, S.: Crowd counting using multiple local features. In: Digital Image Computing: Techniques and Applications, pp. 81–88. IEEE (2009)

25.

Ryan, D., Denman, S., Sridharan, S., Fookes, C.: An evaluation of crowd counting methods, features and regression models. Comput. Vis. Image Underst. 130, 1–17 (2015)CrossRef

26.

Spinello, L., Arras, K.: People detection in RGB-D data. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3838–3843 (2011)

27.

Wang, Y., Lian, H., Chen, P., Lu, Z.: Counting people with support vector regression. In: International Conference on Natural Computation, pp. 139–143. IEEE (2014)

28.

Yan-Yan, C., Ning, C., Yu-Yang, Z., Ke-Han, W., Wei-Wei, Z.:Pedestrian detection and tracking for counting applications in metrostation. Discrete Dyn. Nat. Soc. 2014, 1–11 (2014)

29.

Yu, S., Wu, S., Wang, L.: SLTP: a fast descriptor for people detection in depth images. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 43–47 (2012)

30.

Zhang, C., Zhang, Z.: Calibration between depth and color sensors for commodity depth cameras. In: Computer Vision and Machine Learning with RGB-D Sensors, pp. 47–64. Springer (2014)

31.

Zhang, X., Yan, J., Feng, S., Lei, Z., Yi, D., Li, S.Z.: Water filling: unsupervised people counting via vertical kinect sensor. In: International Conference on Advanced Video and Signal-Based Surveillance, pp. 215–220. IEEE (2012)

32.

Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRef

33.

Zhu, L., Wong, K.-H.: Human tracking and counting using the kinect range sensor based on adaboost and kalman filter. In: Advances in Visual Computing, pp. 582–591. Springer (2013)

34.

Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: International Conference on Pattern Recognition, vol. 2, pp. 28–31. IEEE (2004)

Title: Counting pedestrians with a zenithal arrangement of depth cameras
Authors: Pablo Vera
Sergio Monjaraz
Joaquín Salas
Publication date: 01-02-2016
Publisher: Springer Berlin Heidelberg
Published in: Machine Vision and Applications / Issue 2/2016
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-015-0739-1

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