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research-article

Human position and head direction tracking in fisheye camera using randomized ferns and fisheye histograms of oriented gradients

Authors:

Veerachart Srisamosorn,

Noriaki Kuwahara,

Atsushi Yamashita,

Shouhei Shirafuji,

Jun OtaAuthors Info & Claims

The Visual Computer, Volume 36, Issue 7

Pages 1443 - 1456

https://doi.org/10.1007/s00371-019-01749-9

Published: 01 July 2020 Publication History

Abstract

This paper proposes a system for tracking human position and head direction using fisheye camera mounted to the ceiling. This is believed to be the first system to estimate head direction from ceiling-mounted fisheye camera. Fisheye histograms of oriented gradients descriptor is developed as a substitute to the histograms of oriented gradients descriptor which has been widely used for human detection in perspective camera. Human body and head are detected by the proposed descriptor and tracked to extract head area for direction estimation. Direction estimation using randomized ferns is adapted to work with fisheye images by using the proposed descriptor, guided by the direction of movement. With experiments on available dataset and new dataset with ground truth, the direction can be estimated with average error below

40^{\circ}

, with head position error half of the head size.

References

[1]

Srisamosorn V, Kuwahara N, Yamashita A, Ogata T, and Ota J Human-tracking system using quadrotors and multiple environmental cameras for face-tracking application Int. J. Adv. Robot. Syst. 2017 14 5 1-18

[2]

Yoshimoto, H., Date, N., Yonemoto, S.: Vision-based real-time motion capture system using multiple cameras. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI2003, pp. 247–251 (2003)

[3]

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)

[4]

Dollár P, Wojek C, Schiele B, and Perona P Pedestrian detection: an evaluation of the state of the art IEEE Trans. Pattern Anal. Mach. Intell. 2012 34 4 743-761

[5]

Felzenszwalb PF, Girshick RB, McAllester D, and Ramanan D Object detection with discriminatively trained part-based models IEEE Trans. Pattern Anal. Mach. Intell. 2010 32 9 1627-1645

[6]

Kittipanya-ngam, P., Ong, S.G., Eng, H.L.: Estimation of human body orientation using histogram of oriented gradients. In: 12th IAPR Conference on Machine Vision Applications, pp. 459–462 (2011)

[7]

Liu B, Wu H, Su W, and Sun J Sector-ring HOG for rotation-invariant human detection Signal Process. Image Commun. 2017 54 1-10

[8]

Meinel, L., Findeisen, M., Heß, M., Apitzsch, A., Hirtz, G.: Automated real-time surveillance for ambient assisted living using an omnidirectional camera. In: 2014 IEEE International Conference on Consumer Electronics (ICCE), pp. 396–399 (2014)

[9]

Zhou Z, Chen X, Chung YC, He Z, Han TX, and Keller JM Activity analysis, summarization, and visualization for indoor human activity monitoring IEEE Trans. Circuits Syst. Video Technol. 2008 18 11 1489-1498

[10]

Demiröz, B.E., Arı, I., Eroğlu, O., Salah, A.A., Akarun, L.: Feature-based tracking on a multi-omnidirectional camera dataset. In: 2012 5th International Symposium on Communications, Control and Signal Processing, pp. 1–5 (2012)

[11]

Saito, M., Kitaguchi, K., Kimura, G., Hashimoto, M.: Human detection from fish-eye image by bayesian combination of probabilistic appearance models. In: 2010 IEEE International Conference on Systems, Man, and Cybernetics, pp. 243–248 (2010)

[12]

Chiang, A.T., Wang, Y.: Human detection in fish-eye images using HOG-based detectors over rotated windows. In: 2014 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2014)

[13]

Tang, Y., Li, Y., Bai, T., Zhou, X., Li, Z.: Human tracking in thermal catadioptric omnidirectional vision. In: 2011 IEEE International Conference on Information and Automation, pp. 97–102 (2011)

[14]

Tasson, D., Montagnini, A., Marzotto, R., Farenzena, M., Cristani, M.: FPGA-based pedestrian detection under strong distortions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 65–70 (2015)

[15]

Cinaroglu, I., Bastanlar, Y.: A direct approach for human detection with catadioptric omnidirectional cameras. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 2275–2279 (2014)

[16]

Krams, O., Kiryati, N.: People detection in top-view fisheye imaging. In: 2017 14th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), pp. 1–6 (2017)

[17]

Delibasis, K.K., Plagianakos, V.P., Maglogiannis, I.: Pose recognition in indoor environments using a fisheye camera and a parametric human model. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 470–477 (2014)

[18]

Jalal A, Kim YH, Kim YJ, Kamal S, and Kim D Robust human activity recognition from depth video using spatiotemporal multi-fused features Pattern Recognit. 2017 61 295-308

[19]

Jalal A, Sarif N, Kim JT, and Kim TS Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home Indoor Built Environ. 2013 22 1 271-279

[20]

Nguyen, V.T., Nguyen, T.B., Chung, S.T.: ConvNets and AGMM based real-time human detection under fisheye camera for embedded surveillance. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 840–845 (2016)

[21]

Bensebaa A and Larabi S Direction estimation of moving pedestrian groups for intelligent vehicles Vis. Comput. 2018 34 6 1109-1118

[22]

Benfold, B., Reid, I.: Guiding visual surveillance by tracking human attention. In: Proceedings of the 20th British Machine Vision Conference (2009)

[23]

Özuysal M, Calonder M, Lepetit V, and Fua P Fast keypoint recognition using random ferns IEEE Trans. Pattern Anal. Mach. Intell. 2010 32 3 448-461

[24]

Benfold, B.: The acquisition of coarse gaze estimates in visual surveillance. Ph.D. thesis, Oxford University (2011)

[25]

Chen, C., Odobez, J.M.: We are not contortionists: Coupled adaptive learning for head and body orientation estimation in surveillance video. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1544–1551 (2012)

[26]

Rehder, E., Kloeden, H., Stiller, C.: Head detection and orientation estimation for pedestrian safety. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2292–2297 (2014)

[27]

Yan Y, Ricci E, Subramanian R, Liu G, Lanz O, and Sebe N A multi-task learning framework for head pose estimation under target motion IEEE Trans. Pattern Anal. Mach. Intell. 2016 38 6 1070-1083

[28]

Benfold, B., Reid, I.: Unsupervised learning of a scene-specific coarse gaze estimator. In: 2011 International Conference on Computer Vision, pp. 2344–2351 (2011)

[29]

Chamveha I, Sugano Y, Sugimura D, Siriteerakul T, Okabe T, Sato Y, and Sugimoto A Head direction estimation from low resolution images with scene adaptation Comput. Vis. Image Underst. 2013 117 10 1502-1511

[30]

Hulens, D., Van Beeck, K., Goedemé, T.: Fast and accurate face orientation measurement in low-resolution images on embedded hardware. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016), vol. 4, pp. 538–544. Scitepress (2016)

[31]

Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511–I-518 (2001)

[32]

Kalman REA new approach to linear filtering and prediction problemsJ. Basic Eng.196082135-453931993

[33]

Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2, pp. 28–31 (2004)

[34]

Grupo de Tratamiento de Imágenes, Universidad Politécnica de Madrid (GTI-UPM): PIROPO Database. https://sites.google.com/site/piropodatabase. Last accessed 30 Mar 2018

[35]

Dalal, N.: INRIA Person Dataset. http://pascal.inrialpes.fr/data/human/. Last accessed 22 Aug 2018

[36]

Prisacariu, V., Reid, I.: fastHOG—a real-time GPU implementation of HOG. Technical report 2310/09, Department of Engineering Science, Oxford University (2009)

[37]

Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, and Zisserman A The pascal visual object classes challenge: a retrospective Int. J. Comput. Vis. 2015 111 1 98-136

[38]

Motion Analysis Corporation: Motion Analysis Corporation, the Motion Capture Leader. http://www.motionanalysis.com. Last accessed 21 July 2018

[39]

Kannala J and Brandt SS A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses IEEE Trans. Pattern Anal. Mach. Intell. 2006 28 8 1335-1340

[40]

Sobral, A.: BGSLibrary: An OpenCV C++ background subtraction library. In: IX Workshop de Visão Computacional (WVC’2013). Rio de Janeiro, Brazil (2013). https://github.com/andrewssobral/bgslibrary. Accessed 15 Mar 2018

[41]

Lepetit, V., Özuysal, M., Pilet, J.: Ferns: Planar Object Detection Demo | CVLAB. https://cvlab.epfl.ch/software/ferns. Last accessed 21 Aug 2018

Cited By

Tamura MYoshinaga T(2024)Segmentation-based bounding box generation for omnidirectional pedestrian detectionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02933-840:4(2505-2516)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00371-023-02933-8
Haggui OBayd HMagnier B(2024)Centroid human tracking via oriented detection in overhead fisheye sequencesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02790-540:1(407-425)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-023-02790-5

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Information & Contributors

Information

Published In

cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 36, Issue 7

Jul 2020

209 pages

ISSN:0178-2789

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2020

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Cited By

Tamura MYoshinaga T(2024)Segmentation-based bounding box generation for omnidirectional pedestrian detectionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02933-840:4(2505-2516)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00371-023-02933-8
Haggui OBayd HMagnier B(2024)Centroid human tracking via oriented detection in overhead fisheye sequencesThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02790-540:1(407-425)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00371-023-02790-5

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