Advertisement

A robust algorithm for detecting people in overhead views

  • Imran Ahmed
  • Awais Adnan
Article
  • 132 Downloads

Abstract

In this research a human detection system is proposed in which people are viewed from an overhead camera with a wide angle lens. Due to perspective change a person can have different orientations and sizes at different positions in the scene relative to the optical centre. We exploit this property of the overhead camera and develop a novel algorithm which uses the variable size bounding boxes with different orientations, with respect to the radial distance of the center of the image. In these overhead view images we neither used any assumption about the pose or the visibility of a person nor imposed any restriction about the environment. When compare the results of proposed algorithm with a standard histogram of oriented gradient (HOG) algorithm, we achieve not only a huge gain in overall detection rate but also a significant improvement in reducing spurious detections per image. On average, 9 false detections occur per image. A new algorithm is proposed where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.

Keywords

Person detection Overhead view Wide angle Classification Features extraction 

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, IEEE, vol. 1, pp. 886–893 (2005)Google Scholar
  3. 3.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vis. 38(1), 15–33 (2000)CrossRefMATHGoogle Scholar
  4. 4.
    Doulamis, A., Kosmopoulos, D., Sardis, M., Varvarigou, T.: An architecture for a self configurable video supervision. In: Proceedings of the 1st ACM Workshop on Analysis and Retrieval of Events/Actions and Workflows in Video Streams, ACM, pp. 97–104 (2008)Google Scholar
  5. 5.
    Cohen, I., Garg, A., Huang, T.S.: Vision-based overhead view person recognition. In: 15th International Conference on Pattern Recognition, 2000. Proceedings, vol. 1, pp. 1119–1124 (2000)Google Scholar
  6. 6.
    Aradhye, H., Fischler, M., Bolles, R., Myers, G.: Headprint–person reacquisition using visual features of hair in overhead surveillance video. In: Audio-and Video-Based Biometric Person Authentication, pp. 879–890. Springer, Berlin (2005)Google Scholar
  7. 7.
    Snidaro, L., Micheloni, C., Chiavedale, C.: Video security for ambient intelligence. IEEE Trans. Syst. Man Cybern. A 35(1), 133–144 (2005)CrossRefGoogle Scholar
  8. 8.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRefGoogle Scholar
  9. 9.
    Welch, G., Bishop, G.: An introduction to the kalman filter. Department of Computer Science, University of North Carolina (2006)Google Scholar
  10. 10.
    García, J., Gardel, A., Bravo, I., Lázaro, J.L., Martínez, M., Rodríguez, D.: Directional people counter based on head tracking. IEEE Trans. Ind. Electron. 60(9), 3991–4000 (2013)CrossRefGoogle Scholar
  11. 11.
    Pinho, R.R., Tavares, J.M.R., Correia, M.V.: An improved management model for tracking missing features in computer vision long image sequences (2006)Google Scholar
  12. 12.
    Pinho, R.R., Tavares, J.M.R.: Tracking features in image sequences with Kalman filtering, global optimization, mahalanobis distance and a management model (2009)Google Scholar
  13. 13.
    Fascioli, A., Fedriga, R.I., Ghidoni. S.: Vision-based monitoring of pedestrian crossings. In: 14th International Conference on Image Analysis and Processing, 2007. ICIAP 2007, IEEE, pp. 566–574 (2007)Google Scholar
  14. 14.
    Yahiaoui, T., Meurie, C., Khoudour, L., Cabestaing. F.: A people counting system based on dense and close stereovision. In: Image and Signal Processing, pp. 59–66. Springer, Berlin (2008)Google Scholar
  15. 15.
    Velipasalar, S., Tian, Y.-L., Hampapur, A.: Automatic counting of interacting people by using a single uncalibrated camera. In: 2006 IEEE International Conference on Multimedia and Expo, IEEE, pp. 1265–1268 (2006)Google Scholar
  16. 16.
    Yu, S., Chen, X., Sun, W., Xie, D.: A robust method for detecting and counting people. In: International Conference on Audio, Language and Image Processing, 2008. ICALIP 2008, IEEE, pp. 1545–1549 (2008)Google Scholar
  17. 17.
    van Oosterhout, T., Bakkes, S., Kröse, B.J.A.: Head detection in stereo data for people counting and segmentation. In: VISAPP, pp. 620–625 (2011)Google Scholar
  18. 18.
    Ozturk, O., Yamasaki, T., Aizawa, K.: Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, pp. 1020–1027 (2009)Google Scholar
  19. 19.
    Pang, Y., Yuan, Y., Li, X., Pan, J.: Efficient hog human detection. Signal Process. 91(4), 773–781 (2011)CrossRefMATHGoogle Scholar
  20. 20.
    Ahmed, I., Carter, J.N.: A robust person detector for overhead views. In: 2012 21st International Conference on Pattern Recognition (ICPR), IEEE, pp. 1483–1486 (2012)Google Scholar
  21. 21.
    Tian, Y.-L., Brown, L., Connell, C., Pankanti, S., Hampapur, A., Senior, A., Bolle, R.: Absolute head pose estimation from overhead wide-angle cameras. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003, IEEE, pp. 92–99 (2003)Google Scholar
  22. 22.
    Zhang, Z., Venetianer, P.L., Lipton, A.J.: A robust human detection and tracking system using a human-model-based camera calibration. In: The Eighth International Workshop on Visual Surveillance-VS2008 (2008)Google Scholar
  23. 23.
    Nakatani, R., Kouno, D., Shimada, K., Endo, T.: A person identification method using a top-view head image from an overhead camera. JACIII 16(6), 696–703 (2012)CrossRefGoogle Scholar
  24. 24.
    Tseng, T.-E., Liu, A.-S., Hsiao, P.-H., Huang, C.-M., Fu, L.-C.: Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, pp. 4077–4082 (2014)Google Scholar
  25. 25.
    Drayer, B., Brox, T.: Training deformable object models for human detection based on alignment and clustering. In: European Conference on Computer Vision, pp. 406–420. Springer, Amsterdam (2014)Google Scholar
  26. 26.
    Tikkanen, T.: People detection and tracking using a network of low-cost depth cameras (2016). https://aaltodoc.aalto.fi/bitstream/handle/123456789/12708/master Tikkanen Tommi 2014.pdf. Accessed 27 Apr 2017
  27. 27.
    Del Pizzo, L., Foggia, P., Greco, A., Percannella, G., Vento, M.: A versatile and effective method for counting people on either rgb or depth overhead cameras. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, pp. 1–6 (2015)Google Scholar
  28. 28.
    Ye, Q., Han, Z., Jiao, J., Liu, J.: Human detection in images via piecewise linear support vector machines. IEEE Trans. Image Process. 22(2), 778–789 (2013)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Rauter, M.: Reliable human detection and tracking in top-view depth images. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, pp. 529–534 (2013)Google Scholar
  30. 30.
    Reilly, V., Solmaz, B., Shah, M.: Shadow casting out of plane (scoop) candidates for human and vehicle detection in aerial imagery. Int. J. Comput. Vis. 101(2), 350–366 (2013)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Jiang, Y., Ma, J.: Combination features and models for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 240–248 (2015)Google Scholar
  32. 32.
    Choi, T.-W., Kim, D.-H., Kim, K.-H.: Human detection in top-view depth image (2016)Google Scholar
  33. 33.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV Library. O’Reilly Media, Inc., Sebastopol (2008)Google Scholar
  34. 34.
    Laganière, R.: OpenCV 2 computer vision application programming cookbook. Packt Publishing Ltd. (2011)Google Scholar
  35. 35.
    Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The det curve in assessment of detection task performance. Technical report, DTIC Document (1997)Google Scholar
  36. 36.
    Gunn, S.R.: Support vector machines for classification and regression. ISIS technical report (1998)Google Scholar
  37. 37.
    Bennett, K.P., Campbell, C.: Support vector machines: hype or hallelujah? ACM SIGKDD Explor. Newsl. 2(2), 1–13 (2000)CrossRefGoogle Scholar
  38. 38.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1999)MATHGoogle Scholar
  39. 39.
    Chang, C., Lin, C.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  40. 40.
    Dalal, N.: Finding people in images and videos. PhD thesis, Institut National Polytechnique de Grenoble-INPG (2006)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Centre of Excellence in ITInstitute of Management SciencesPeshawarPakistan

Personalised recommendations