Signal, Image and Video Processing

, Volume 8, Supplement 1, pp 125–134 | Cite as

An efficient HOG–ALBP feature for pedestrian detection

  • Yifeng Liu
  • Lin ZengEmail author
  • Yan Huang
Original Paper


Histograms of oriented gradients (HOG) is the most successful feature descriptor in pedestrian detection; however, it is limited because of only considering the gradient. It has a certain false-positive rate on some examples, which have a lot of parallel vertical components (looks like a leg or a body) due to lacking of texture feature. This paper proposes a method to combine a cell-structured HOG feature and adaptive local binary pattern feature to solve the problem that HOG is vulnerable to the interference of vertical background gradient information in pedestrian detection. In addition, we use a fast method to utilize sub-cell-based interpolation to efficiently compute HOG feature for each block. Training the combination feature to get a discriminative model by bootstrapped linear support vector machine. Experimental results on the INRIA dataset have demonstrated the effectiveness and efficiency of the proposed method.


Pedestrian detection HOG ALBP  Vertical background gradient Bootstrapped SVM 



This work is supported by “the Fundamental Research Funds for the Central Universities” (No. 2014212020202).


  1. 1.
    Chua, J.-L., Chang, Y.C., Lim, W.K.: A simple vision-based fall detection technique for indoor video surveillance. Signal Image Video Process. 1–11 (2013). doi: 10.1007/s11760-013-0493-7
  2. 2.
    Tao, D., Li, X., Wu, X., Maybank, S.J.: Human carrying status in visual surveillance. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1670–1677 (2006)Google Scholar
  3. 3.
    Jin, Z., Lou, Z., Yang, J., Sun, Q.: Face detection using template matching and skin-color information. Neurocomputing 60(4–6), 794–800 (2007)CrossRefGoogle Scholar
  4. 4.
    Espinace, P., Kollar, T., Roy, N., Soto, A.: Indoor scene recognition by a mobile robot through adaptive object detection. Rob. Auton. Syst. 61(9), 932–947 (2013)CrossRefGoogle Scholar
  5. 5.
    Tao, D., Li, X., Wu, X., Maybank, S.J.: Elapsed time in human gait recognition: a new approach. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 177–180 (2006)Google Scholar
  6. 6.
    Guan, Y., Li, C.-T., Choudhury, S.D.: Robust gait recognition from extremely low frame-rate videos. In: Proceedings of International Workshop on Biometrics and Forensics (2013)Google Scholar
  7. 7.
    Schick, B., Schmidt, S.: Evaluation of video-based driver assistance systems with sensor data fusion by using virtual test driving. In: Proceedings of the FISITA 2012 World Automotive Congress Lecture Notes in, Electrical Engineering, vol. 196, pp. 1363–1375 (2013)Google Scholar
  8. 8.
    Xiao, B., Gao, X., Tao, D., Li, X.: A new approach for face recognition by sketches in photos. Signal Process. 89(8), 1576–1588 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Tasdemir, K., Cetin, A.E.: Motion vector based features for content based video copy detection. In: Proceedings of IEEE 20th International Conference on Pattern Recognition (ICPR), pp. 3134–3137 (2010)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 886–893 (2005)Google Scholar
  11. 11.
    Walk, S., Majer, N., Schindler, K., et al.: New features and insights for pedestrian detection. In: IEEE Computer, Society Conference on Computer Vision and Pattern Recognition, pp. 1030–1037 (2010)Google Scholar
  12. 12.
    Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for human detection. IPSJ Trans. Comput. Vis. Appl. 2, 39–47 (2010)Google Scholar
  13. 13.
    Bilgic, B.: Fast Human Detection with Cascaded Ensembles. Massachusetts Institute of Technology, Massachusetts (2010)Google Scholar
  14. 14.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, NewYork (1995)CrossRefzbMATHGoogle Scholar
  15. 15.
    Liu, H., Tao, X.: Related HOG features for human detection using cascaded adaboost and SVM classifiers. Adv. Multimed. Model. Lect. Notes Comput. Sci. 7733, 345–355 (2013)CrossRefGoogle Scholar
  16. 16.
    Zhu, Q., Yeh, M.-C., Cheng, K.-T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1491–1498 (2006)Google Scholar
  17. 17.
    PORIKLI, F.: Integral histogram: a fast way to extract histograms in Cartesian spaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 829–836 (2005)Google Scholar
  18. 18.
    David, G.: Lowe distinctive image features for scale-invariant key points. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  19. 19.
    Dalal, N.: Finding people in images and videos. Ph.D. thesis, INRIA Rhone-Alpes (2006)Google Scholar
  20. 20.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)zbMATHGoogle Scholar
  21. 21.
    Burges, C.J.C.: A tutorial on support vector machine for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)CrossRefGoogle Scholar
  22. 22.
    Pang, Y., Yuan, Y., Li, X., Pan, J.: Efficient HOG human detection. Signal Process. 91(4), 773–781 (2011)CrossRefzbMATHGoogle Scholar
  23. 23.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classfication based on feature distributions. Pattern Recognit. 29(1), 51–59 (1998)CrossRefGoogle Scholar
  24. 24.
    Goyal, A., Walia, E.: Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval. Signal Image Video Process. 1–17 (2012). doi: 10.1007/s11760-012-0353-x
  25. 25.
    Paulhac, L., Makris, P., Ramel, J.-Y., Gregoire, J.-M.: A framework of perceptual features for the characterisation of 3D textured images. Signal Image Video Process. 1–25 (2013). doi: 10.1007/s11760-013-0438-1
  26. 26.
    Guo, Z.H.H., Zhang, L., Zhang, D.: A Completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Mu, Y., Yan, S., Liu, Y., Huang, T., Zhou, B.: Discriminative local binary patterns for human detection in personal album. In: CVPR (2008)Google Scholar
  28. 28.
    Ahonen, T., Hadid, A., Pietikinen, M.: Face recognition with local binary patterns. In: ECCV, pp. 469–481 (2004)Google Scholar
  29. 29.
    Alonso-Atienza, F., Rojo-Álvarez, J.L., Rosado-Muñoz, A., Vinagre, J.J., García-Alberola, A., Camps-Valls, G.: Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection. Expert Syst. Appl. 39(2), 1956–1967 (2012)CrossRefGoogle Scholar
  30. 30.
    Chang, C.-C., Lin, C.-J.: A Library for Support vector machine. Department of Computer Science, National Taiwan University, Taipei (2011)Google Scholar
  31. 31.
    Fan, R.E., Chang, K.W., Hsieh, C.J., et al.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)zbMATHGoogle Scholar
  32. 32.
    Wang, H.., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009-British Machine Vision Conference (2009)Google Scholar
  33. 33.
    Kobi, Levi., Weiss, Yair.: Learning object detection from a small number of examples: the importance of good features. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004, vol. 2:II-53. IEEE (2004)Google Scholar
  34. 34.
    Gerońimo, D., Lopez, A., Ponsa, D., Sappa, A.D.: Haar wavelets and edge orientation histograms for on-board pedestrian detection. In: Pattern Recognition and Image Analysis Lecture Notes in Computer Science, vol. 4477, pp. 418–425 (2007)Google Scholar
  35. 35.
    Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR, pp. 1–8 (2007)Google Scholar
  36. 36.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable partmodel. In: CVPR, (2008)Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.School of Electronic InformationWuhan UniversityWuhan China

Personalised recommendations