Wavelet based iterative deformable part model for pedestrian detection

  • S. D. GovardhanEmail author
  • A. Vasuki


Pedestrian detection is one of the challenging tasks in the urban traffic environments. A natural urban traffic environments include different objects like buildings, vehicles, pedestrians and so on. The conventional approach only used for a particular traffic scenario and it does not suitable for different scenarios. A novel approach is required to model these traffic scenarios. Multiresolution Morlet Decomposition Based Iterative Learning Deformable Part Model (MMD-ILDP) is proposed for improving the performance of multiresolution pedestrian detection to control the traffic in the urban area with higher accuracy. The MMD-ILDP Model uses Morlet wavelet transformation for decomposing the image into subbands with multiple resolutions. After wavelet decomposition, histogram of oriented gradients (HOG) feature pyramid is generated. Then, feature matching is performed between the pedestrian objects in the image and the feature pyramid generated with HOG and the root and part scores are computed. Finally, the root and part scores are combined to compute the final score of objects in the image. The performance measures used in evaluating the proposed algorithm are detection accuracy, time and space complexity. The simulation results show that the MMD-ILDP Model gives improved pedestrian detection in urban traffic environment where healthcare systems find more difficult to reach to the people and also reduces the time complexity on detecting the pedestrians in road traffic scenes when compared to the existing DPM and RealBoost methods.


Feature pyramid Iterative learning deformable part model Jaccard similarity score Morlet Multiresolution analysis Pedestrian detection 



  1. 1.
    Afrakhteh M, Miryong P (2017) Pedestrian detection with minimal false positives per color-thermal image. Arab J Sci Eng Springer 42(8):3207–3219CrossRefGoogle Scholar
  2. 2.
    Biswas SK, Milanfar P (2017) Linear support tensor machine with LSK channels: pedestrian detection in thermal infrared images. IEEE Trans Image Process 26(9):4229–4242MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chen Y, Zhang L, Liu X, Chen C (2016) Pedestrian detection by learning a mixture mask model and its implementation. Inf Sci Elsevier 372(1):148–161CrossRefGoogle Scholar
  4. 4.
    Choi HJ, Lee YS, Shim D-S, Lee CG, Choi KN (2016) Effective pedestrian detection using deformable part model based on human model. Int J Control Autom Syst Springer 14(6):1618–1625CrossRefGoogle Scholar
  5. 5.
    Hong G-S, Kim B-G, Hwang Y-S, Kwon K-K (2016) Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform. Multimed Tools Appl Springer 75(23):15229–15245CrossRefGoogle Scholar
  6. 6.
    Jiang X, Pang Y, Li X, Pan J (2016) Speed up deep neural network based pedestrian detection by sharing features across multi-scale models. Neurocomputing 185:163–170CrossRefGoogle Scholar
  7. 7.
    Kim H-K, Kim D (2017) Robust pedestrian detection under deformation using simple boosted features. Image Vis Comput Elsevier 61:1–11CrossRefGoogle Scholar
  8. 8.
    Kwak J-Y, Ko BC, Nam JY (2017) Pedestrian tracking using online boosted random ferns learning in far-infrared imagery for safe driving at night. IEEE Trans Intell Transp Syst 18(1):69–81CrossRefGoogle Scholar
  9. 9.
    Li J, Gong W, Li W, Liu X (2010) Robust pedestrian detection in thermal infrared imagery using the wavelet transform. Infrared Phys Technol Elsevier 53:267–273CrossRefGoogle Scholar
  10. 10.
    Li K, Wang X, Xu Y, Wang J (2016) Density enhancement-based long-range pedestrian detection using 3-D range data. IEEE Trans Intell Transp Syst 17(5):1368–1380CrossRefGoogle Scholar
  11. 11.
    Li C, Wang X, Liu W (2017) Neural features for pedestrian detection. Neurocomputing, Elsevier 238:420–432CrossRefGoogle Scholar
  12. 12.
    Liu Y, Lasang P, Siegel M, Sun Q (2016) Multi-sparse descriptor: a scale invariant feature for pedestrian detection. Neurocomputing, Elsevier 184:55–65CrossRefGoogle Scholar
  13. 13.
    Luo Y, Yin D, Wang A, Wu W (2018) Pedestrian tracking in surveillance video based on modified CNN. Multimed Tools Appl 1–18Google Scholar
  14. 14.
    Maggiani L, Bourrasset C, Quinton J-C, Berry F, Sérot J (2016) Bio-inspired heterogeneous architecture for real-time pedestrian detection applications. J Real-Time Image Proc Springer 1–14Google Scholar
  15. 15.
    Ouyang W, Zeng X, Wang X (2016) Partial occlusion handling in pedestrian detection with a deep model. IEEE Trans Circuits Syst Video Technol 26(11):2123–2137CrossRefGoogle Scholar
  16. 16.
    Ouyang W, Zeng X, Wang X (2016) Learning mutual visibility relationship for pedestrian detection with a deep model. Int J Comput Vis Springer 120(1):14–27MathSciNetCrossRefGoogle Scholar
  17. 17.
    Paisitkriangkrai S, Shen C, van den Hengel A (2016) Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Trans Pattern Anal Mach Intell 38(6):1243–1257CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Wang Y, Pierard S, Su S-Z, Jodoin P-M (2017) Improving pedestrian detection using motion-guided filtering. Pattern Recogn Lett Elsevier 96(1):106–112CrossRefGoogle Scholar
  20. 20.
    Wei X, Lu W, Bao P, Xing W (2018) MGA for feature weight learning in SVM —a novel optimization method in pedestrian detection. Multimed Tools Appl Springer 77(7):9021–9037CrossRefGoogle Scholar
  21. 21.
    Yan J, Zhang X, Lei Z, Liao S, Li SZ (2013) Robust multi-resolution pedestrian detection in traffic scenes. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2013:3033–3040Google Scholar
  22. 22.
    Yang D, Zhang J, Xu S, Ge S, Hemantha Kumar G, Zhang X (2018) Real-time pedestrian detection via hierarchical convolutional feature. Multimed Tools Appl Springer 1–20Google Scholar
  23. 23.
    Zhang S, Klein DA, Bauckhage C, Cremers AB (2016) Fast moving pedestrian detection based on motion segmentation and new motion features. Multimed Tools Appl Springer 75(11):6263–6282CrossRefGoogle Scholar
  24. 24.
    Zhang G, Jiang P, Matsumoto K, Yoshida M, Kita K (2017) An improvement of pedestrian detection method with multiple resolutions. J Comput Commun 5:102–116CrossRefGoogle Scholar
  25. 25.
    Zhou Z-M, Zhao X (2016) Parallelized deformable part models with effective hypothesis pruning. Comput Vis Media Springer 2(3):245–256CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Department of ECECoimbatore Institute of Engineering and TechnologyCoimbatoreIndia
  2. 2.Department of MCEKumaraguru College of TechnologyCoimbatoreIndia

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