Multimedia Tools and Applications

, Volume 77, Issue 23, pp 30891–30910 | Cite as

Real-time moving pedestrian detection using contour features

  • Kai Zhao
  • Jingjing Deng
  • Deqiang ChengEmail author


Pedestrian detection is one of the most fundamental research in computer vision. However, many high performance detectors run slowly. In this paper, we propose a real-time moving pedestrian detector by using efficient contour features. Firstly, the moving targets are detected by background subtraction. By combining the elliptic Fourier descriptors and the normalized central moments, we propose the Elliptic Fourier and Moments Descriptors (EFMD) to describe the moving target contours. Secondly, the moving targets are classified by the trained Support Vector Machine (SVM). In addition, we introduce a novel overlap handling algorithm based on linear fitting and normalized central moments, which improves the detection performance by reducing both false positives and miss rate. The experimental results on PETS 2009 and CAVIAR datasets show that our approach achieves a miss rate of 14% (PETS 2009) and 13% (CAVIAR) at 10−1 False Positives Per Image (FPPI) and an average runtime per frame of 30 ms (PETS 2009) and 25 ms (CAVIAR), which significantly outperforms several state-of-the-art detectors in both detection performance and runtime.


Pedestrian detection Elliptic Fourier descriptors Normalized central moments Support vector machine Linear fit 



This work was supported by the Fundamental Research Funds for the Central Universities (No.2014ZDPY32).


  1. 1.
    Barron JL, Fleet DJ, Beauchemin SS, Burkitt TA (1994) Performance of Optical Flow Techniques. Int J Comput Vis 12(1):43–77CrossRefGoogle Scholar
  2. 2.
    Chang X, Yang Y (2017) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw Learn Syst 28(10):2294–2305MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chang X, Ma Z, Lin M, Yang Y, Hauptmann A (2017) Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection. IEEE Trans Image Process 26(8):3911–3920MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann A (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180–1197CrossRefGoogle Scholar
  5. 5.
    Chang X, Yu YL, Yang Y, Xing EP (2017) Semantic pooling for complex event analysis in untrimmed videos. IEEE Transactions on Pattern Analysis & Machine Intelligence 39(8):1617–1632CrossRefGoogle Scholar
  6. 6.
    Dalal N, Triggs B (2005) Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision & Pattern Recognition 1(12):886–893Google Scholar
  7. 7.
    Dollár P, Tu Z, Perona P, Belongie S (2009) Integral channel Features. British Machine Vision Conference, BMVC 2009, London, UK, September 7-10, 2009. Proceedings DBLPGoogle Scholar
  8. 8.
    Dollár P, Belongie S, Perona P (2010) The fastest pedestrian detector in the West. British Machine Vision Conference, BMVC 2010, Aberystwyth, UK, August 31 - September 3, 2010. Proceedings DBLP, 2010:1–11Google Scholar
  9. 9.
    Dollár P, Wojek C, Schiele B, Perona P (2011) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761CrossRefGoogle Scholar
  10. 10.
    Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545CrossRefGoogle Scholar
  11. 11.
    Gavrila DM (2007) A Bayesian Exemplar-based approach to hierarchical shape matching. IEEE Trans Pattern Anal Mach Intell 29(8):1408–1421CrossRefGoogle Scholar
  12. 12.
    Granlund GH (1972) Fourier preprocessing for hand print character recognition. IEEE Trans Comput 21:195–201MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hinton GE (2009) Deep belief networks. Scholarpedia 4(6):5947CrossRefGoogle Scholar
  14. 14.
    Li Z, Nie F, Chang X, Yang Y (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Trans Knowl Data Eng 99:1–1Google Scholar
  15. 15.
    Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2008) Learning Multi-scale Block Local Binary Patterns for Face Recognition. Advances in Biometrics 4642:828–837CrossRefGoogle Scholar
  16. 16.
    Lin Z, Davis LS (2008) A pose-invariant descriptor for human detection and segmentation. European Conference on Computer Vision Springer-Verlag, 2008:423–436Google Scholar
  17. 17.
    Liu Y, Chen X, Yao H, Cui X, Liu C, Gao W (2009) Contour-motion feature (CMF): A space–time approach for robust pedestrian detection. Pattern Recogn Lett 30(2):148–156CrossRefGoogle Scholar
  18. 18.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110MathSciNetCrossRefGoogle Scholar
  19. 19.
    Mohan A, Papageorgiou C, Poggio T (2001) Example-Based Object Detection in Images by Components. IEEE Trans Pattern Anal Mach Intell 23(4):349–361CrossRefGoogle Scholar
  20. 20.
    Paisitkriangkrai S, Shen C, Hengel AVD (2016) Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. IEEE Trans Pattern Anal Mach Intell 38(6):1243–1257CrossRefGoogle Scholar
  21. 21.
    Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vis 38(1):15–33CrossRefGoogle Scholar
  22. 22.
    Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. International Conference on Neural Information Processing Systems 39:91–99Google Scholar
  23. 23.
    Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. Computer Vision and Pattern Recognition, 2007. IEEE Conference on IEEE, 2007:1–8Google Scholar
  24. 24.
    Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. IEEE Conference on Computer Vision and Pattern Recognition IEEE Computer Society, 2013:3626–3633Google Scholar
  25. 25.
    Shen J, Zuo X, Li J, Yang W, Ling H (2017) A novel pixel neighborhood differential statistic feature for pedestrian and face detection. Pattern Recogn 63:127–138CrossRefGoogle Scholar
  26. 26.
    Stauffer C, Grimson WE (1999) Adaptive background mixture models for realtime tracking. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on IEEE Xplore, 1999(2):252Google Scholar
  27. 27.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  28. 28.
    Walk S, Majer N, Schindler K, Schiele B (2010) New features and insights for pedestrian detection. IEEE Conference on Computer Vision and Pattern Recognition 2010, 119(5):1030–1037Google Scholar
  29. 29.
    Wang X, Han TX (2009) An HOG-LBP human detector with partial occlusion handling. Proc.IEEE Int.conf.on Computer Vision Kyoto Japan Sept, 30(2):32–39Google Scholar
  30. 30.
    Wojek C, Schiele B (2008) A performance evaluation of single and multi-feature people detection. In: Proceedings of the symposium of the german association for pattern recognition (DAGM)Google Scholar
  31. 31.
    Wu B, Nevatia R (2008) Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. Computer Vision and Pattern Recognition, 2008. IEEE Conference on IEEE, 2008:1–8Google Scholar
  32. 32.
    You X, Du L, Cheung YM, Chen Q (2010) A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans Image Process 19(12):3271–3284MathSciNetCrossRefGoogle Scholar
  33. 33.
    Zhang S, Bauckhage C, Klein D, Cremers A (2016) Fast moving pedestrian detection based on motion segmentation and new motion features. Multimed Tools Appl 75(11):6263–6282CrossRefGoogle Scholar
  34. 34.
    Zhu Z, You X, Chen CL, Tao D, Ou W, Jiang X, Zou J (2015) An adaptive hybrid pattern for noise-robust texture analysis. Pattern Recogn 48:2592–2608CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouPeople’s Republic of China

Personalised recommendations