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Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

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Abstract

This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in what can be detected, a combination of visual cues is used in conjunction with edge-based features and colour information as a basis for training a random forest (RF) classifier to detect the local contour cues for pedestrian images. To achieve a real-time detection rate, the contour cues, edge-based features and colour information are incorporated and then trained using a cascade RF classifier with a census transform histogram visual descriptor that implicitly captures the global contours of the pedestrians. The contour detector favourably exceeded previous leading contour detectors and achieved a 95% detection rate. The reliability and specificity of the pedestrian detector are demonstrated on more than 5000 positive images containing street furniture, lamp posts and trees, structures that are frequently confused with persons by computer vision systems. Evaluation with over 220 video sequences with \(640 \times 480\) pixel resolution presented a true positive rate of 96%. The proposed pedestrian detector outperforms previous competitive pedestrian detectors on many varied person data sets. The speed of execution in a robot is about 62 ms per frame for images of \(640 \times 480\) pixels on an Intel Core i3-2310M™ processor running at 2.10 GHz with a RAM of 4 GB.

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References

  1. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  2. Benfold B, Reid I (2011) Unsupervised learning of a scene-specific coarse gaze estimator. In: 2011 international conference on computer vision, pp 2344–2351

  3. Braik M, Pycock D (2013) Pedestrian cue detection: colour inverse maximum likelihood ratio. In: Sixth international conference on machine vision (ICMV 2013). International society for optics and photonics, vol 9067

  4. Braik MS (2016) A highly adaptable model based-method for colour image interpretation. Ph.D. thesis, University of Birmingham

  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  6. Breiman L, Cutler A (2014) Random forests-classification description: random forests. Available online: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. Accessed 12 Feb 2018

  7. Canny J (1987) A computational approach to edge detection. In: Readings in computer vision. Elsevier, Amsterdam, pp 184–203

  8. Cao Y, Guan D, Huang W, Yang J, Cao Y, Qiao Y (2019) Pedestrian detection with unsupervised multispectral feature learning using deep neural networks. Inf Fus 46:206–217

    Article  Google Scholar 

  9. Ciabattoni L, Foresi G, Monteriù A, Pepa L, Pagnotta DP, Spalazzi L, Verdini F (2019) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and ble beacons. J Ambient Intell Humaniz Comput 10(1):1–12

    Article  Google Scholar 

  10. Dalal N, Triggs, B (2005) Histograms of oriented gradients for human detection. In: International conference on computer vision and pattern recognition (CVPR’05). IEEE Computer Society, vol 1, pp 886–893

  11. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  12. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  15. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  Google Scholar 

  16. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  Google Scholar 

  17. Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158

    Article  Google Scholar 

  18. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MathSciNet  MATH  Google Scholar 

  19. Hosang J, Omran M, Benenson R, Schiele B (2015) Taking a deeper look at pedestrians. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4073–4082

  20. Jiang Y, Wang J, Liang Y, Xia J (2019) Combining static and dynamic features for real-time moving pedestrian detection. Multimed Tools Appl 78(3):3781–3795

    Article  Google Scholar 

  21. Kim T, Kim S (2018) Pedestrian detection at night time in fir domain: comprehensive study about temperature and brightness and new benchmark. Pattern Recognit 79:44–54

    Article  Google Scholar 

  22. Li J, Liang X, Shen S, Xu T, Feng J, Yan S (2018) Scale-aware fast R-CNN for pedestrian detection. IEEE Trans Multimed 20(4):985–996

    Google Scholar 

  23. Lim JJ, Zitnick CL, Dollár P (2013) Sketch tokens: a learned mid-level representation for contour and object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3158–3165

  24. Mateus A, Ribeiro D, Miraldo P, Nascimento JC (2019) Efficient and robust pedestrian detection using deep learning for human-aware navigation. Robot Auton Syst 113:23–37

    Article  Google Scholar 

  25. Ouyang, W, Wang X (2013) Joint deep learning for pedestrian detection. In: Proceedings of the IEEE international conference on computer vision, pp 2056–2063

  26. Ouyang W, Zhou H, Li H, Li Q, Yan J, Wang X (2018) Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE Trans Pattern Anal Mach Intell 40(8):1874–1887

    Article  Google Scholar 

  27. Papari G, Petkov N (2011) An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection. Pattern Recognit 44(9):1999–2007

    Article  Google Scholar 

  28. Ruzon MA, Tomasi C (1999) Color edge detection with the compass operator. In: Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), vol 2, pp 160–166

  29. Schwartz WR, Kembhavi A, Harwood D, Davis LS (2009) Human detection using partial least squares analysis. In: 2009 IEEE 12th international conference on computer vision, pp 24–31

  30. Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3626–3633

  31. Sheta A, Alkasassbeh M, Braik M, Ayyash HA (2012) Detection of oil spills in SAR images using threshold segmentation algorithms. Int J Comput Appl 57(7):10–15

    Google Scholar 

  32. Sheta A, Braik MS, Aljahdali S (2012) Genetic algorithms: a tool for image segmentation. In: 2012 international conference on multimedia computing and systems, pp 84–90

  33. Soundrapandiyan R, Mouli PC (2017) Adaptive pedestrian detection in infrared images using fuzzy enhancement and top-hat transform. Int J Comput Vis Robot 7(1–2):49–67

    Article  Google Scholar 

  34. Tian Y, Luo P, Wang X, Tang X (2015) Pedestrian detection aided by deep learning semantic tasks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5079–5087

  35. Vandoni J, Aldea E, Le Hégarat-Mascle S (2019) Evidential query-by-committee active learning for pedestrian detection in high-density crowds. Int J Approx Reason 104:166–184

    Article  MathSciNet  Google Scholar 

  36. Viola P, Jones MJ, Snow D (2005) Detecting pedestrians using patterns of motion and appearance. Int J Comput Vis 63(2):153–161

    Article  Google Scholar 

  37. Wang L, Shi J, Song G, Shen IF (2007) Object detection combining recognition and segmentation. In: Asian conference on computer vision. Springer, Berlin, pp 189–199

  38. Wu J, Geyer C, Rehg JM (2011) Real-time human detection using contour cues. In: 2011 IEEE international conference on robotics and automation, pp 860–867

  39. Zhang C, Ruan X, Zhao Y, Yang MH (2012) Contour detection via random forest. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 2772–2775

  40. Zhang H, Cao X, Ho JK, Chow TW (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520–531

    Article  Google Scholar 

  41. Zhang H, Wang S, Xu X, Chow TW, Wu QJ (2018) Tree2vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 99:1–15

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Al-Balqa Applied University, Al-Salt, 19117, Jordan

    Malik Braik & Heba Al-Hiary

  2. German Jordanian University, Amman, 11180, Jordan

    Hussein Al-Zoubi

  3. Yarmouk University, Irbid, 21163, Jordan

    Hussein Al-Zoubi

Authors
  1. Malik Braik
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  2. Hussein Al-Zoubi
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  3. Heba Al-Hiary
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Correspondence to Hussein Al-Zoubi.

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Braik, M., Al-Zoubi, H. & Al-Hiary, H. Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier. Pattern Anal Applic 23, 751–769 (2020). https://doi.org/10.1007/s10044-019-00835-x

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