Multimedia Tools and Applications

, Volume 78, Issue 6, pp 7029–7053 | Cite as

Geometrical-based approach for robust human image detection

  • Obaida M. Al-HazaimehEmail author
  • Malek Al-Nawashi
  • Mohamad Saraee


In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches.


Human classification Geometrical model INRIA Machine learning SVM ANN Random forest 



  1. 1.
    Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. Computer Vision Systems 66–75Google Scholar
  2. 2.
    Al-Abri M, Hilal N (2008) Artificial neural network simulation of combined humic substance coagulation and membrane filtration. Chem Eng J 141:27–34CrossRefGoogle Scholar
  3. 3.
    Al-Hazaimeh OMA (2012) Hiding data in images using new random technique. IJCSI Int J Comput Sci Issues 9:49–53Google Scholar
  4. 4.
    Al-hazaimeh OM (2014) A novel encryption scheme for digital image-based on one dimensional logistic map. Comput Inf Sci 7:65Google Scholar
  5. 5.
    Al-Nawashi M, Al-Hazaimeh OM, Saraee M (2016) A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments. Neural Comput & Applic:1–8Google Scholar
  6. 6.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33:898–916CrossRefGoogle Scholar
  7. 7.
    Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27:1485–1490CrossRefGoogle Scholar
  8. 8.
    Benayed Y, Fohr D, Haton JP, Chollet G (2003) Confidence measures for keyword spotting using support vector machines. In: Acoustics, Speech, and Signal Processing. Proceedings.(ICASSP'03). 2003 IEEE International Conference on, pp. I-IGoogle Scholar
  9. 9.
    Broggi A, Bertozzi M, Fascioli A, Sechi M (2000) Shape-based pedestrian detection,” in Intelligent Vehicles Symposium, 2000. IV 2000. Proceedings of the IEEE, pp. 215–220Google Scholar
  10. 10.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43:996–1002CrossRefGoogle Scholar
  11. 11.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, pp. 886–893Google Scholar
  12. 12.
    Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In European conference on computer vision. pp. 428–441CrossRefGoogle Scholar
  13. 13.
    Desa SM, Salih QA (2004) Image subtraction for real time moving object extraction. In: Computer Graphics, Imaging and Visualization, 2004. CGIV 2004. Proceedings. International Conference on, pp. 41–45Google Scholar
  14. 14.
    Drayer B, Brox T (2014) Training deformable object models for human detection based on alignment and clustering. In European Conference on Computer Vision, pp. 406–420Google Scholar
  15. 15.
    Gall J, Yao A, Razavi N, Van Gool L, Lempitsky V (2011) Hough forests for object detection, tracking, and action recognition. IEEE Trans Pattern Anal Mach Intell 33:2188–2202CrossRefGoogle Scholar
  16. 16.
    Guan P, Weiss A, Balan AO, Black MJ (2009) Estimating human shape and pose from a single image. In Computer Vision, 2009 IEEE 12th International Conference on, pp. 1381–1388Google Scholar
  17. 17.
    Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28:657–662CrossRefGoogle Scholar
  18. 18.
    Hjelmås E, Low BK (2001) Face detection: a survey. Comput Vis Image Underst 83:236–274CrossRefGoogle Scholar
  19. 19.
    Huang J, Lu J, Ling CX (2003) Comparing naive Bayes, decision trees, and SVM with AUC and accuracy. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pp. 553–556Google Scholar
  20. 20.
    INRIA Person Dataset. (2018) Available:
  21. 21.
    Jacques JCS, Musse SR (2015) Improved head-shoulder human contour estimation through clusters of learned shape models. In Graphics, Patterns and Images (SIBGRAPI), 2015 28th SIBGRAPI Conference on, pp. 329–336Google Scholar
  22. 22.
    Jain H, Subramanian A, Das S, Mittal A (2011) Real-time upper-body human pose estimation using a depth camera. Computer Vision/Computer Graphics Collaboration Techniques, pp. 227–238Google Scholar
  23. 23.
    Kampmann M (1998) Segmentation of a head into face, ears, neck and hair for knowledge-based analysis-synthesis coding of videophone sequences. In Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, pp. 876–880Google Scholar
  24. 24.
    Lakshmi S, Sankaranarayanan DV (2010) A study of edge detection techniques for segmentation computing approaches. IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT, pp. 35–40CrossRefGoogle Scholar
  25. 25.
    Li H, Ngan KN (2008) Saliency model-based face segmentation and tracking in head-and-shoulder video sequences. J Vis Commun Image Represent 19:320–333CrossRefGoogle Scholar
  26. 26.
    Liu Y, Cui J, Zhao H,Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: Pattern Recognition (ICPR), 2012 21st International Conference on, pp. 898–901Google Scholar
  27. 27.
    Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870CrossRefGoogle Scholar
  28. 28.
    Malik J, Belongie S, Leung T, Shi J (2001) Contour and texture analysis for image segmentation. Int J Comput Vis 43:7–27CrossRefGoogle Scholar
  29. 29.
    Marín J, Vázquez D, López AM, Amores J, Kuncheva LI (2014) Occlusion handling via random subspace classifiers for human detection. IEEE Trans Cybern 44:342–354CrossRefGoogle Scholar
  30. 30.
    Michalski RS, Carbonell JG, Mitchell TM (2013) Machine learning: an artificial intelligence approach. Springer Science & Business Media, Tioga, Palo Alto, CA. CrossRefGoogle Scholar
  31. 31.
    Modi RV, Mehta TB (2011) Neural Network based Approach for Recognition Human Motion using Stationary Camera. International Journal of Computer Applications (0975–8887) VolumeGoogle Scholar
  32. 32.
    Moeslund TB, Hilton A, Krüger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104:90–126CrossRefGoogle Scholar
  33. 33.
    Mukherjee S, Das K (2013) A novel equation based classifier for detecting human in images. arXiv preprint arXiv:1307.5591Google Scholar
  34. 34.
    Murray D, Basu A (1994) Motion tracking with an active camera. IEEE Trans Pattern Anal Mach Intell 16:449–459CrossRefGoogle Scholar
  35. 35.
    Obaida MA-H (2015) Combining audio samples and image frames for enhancing video security. Indian Journal of Science and Technology 8:940CrossRefGoogle Scholar
  36. 36.
    Piccardi M (2004) Background subtraction techniques: a review. In Systems, man and cybernetics, 2004 IEEE international conference on, pp. 3099–3104Google Scholar
  37. 37.
    Satpathy A, Jiang X, Eng H-L (2014) Human detection by quadratic classification on subspace of extended histogram of gradients. IEEE Trans Image Process 23:287–297MathSciNetCrossRefGoogle Scholar
  38. 38.
    Sugandi B, Kim H, Tan JK, Ishikawa S (2007) Tracking of moving objects by using a low resolution image. In Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on, pp. 408–408Google Scholar
  39. 39.
    Tan X, Chen S, Zhou Z-H, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39:1725–1745CrossRefGoogle Scholar
  40. 40.
    Tsai D-M (1995) A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recogn Lett 16:653–666CrossRefGoogle Scholar
  41. 41.
    Watanabe T, Ito S, Yokoi K (2009) Co-occurrence histograms of oriented gradients for pedestrian detection. In Pacific-Rim Symposium on Image and Video Technology, pp. 37–47CrossRefGoogle Scholar
  42. 42.
    Wong K-W, Lam K-M, Siu W-C (2001) An efficient algorithm for human face detection and facial feature extraction under different conditions. Pattern Recogn 34:1993–2004CrossRefGoogle Scholar
  43. 43.
    Xia L, Chen C-C, Aggarwal JK (2011) Human detection using depth information by kinect. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, pp. 15–22Google Scholar
  44. 44.
    Xie X, Livermore C (2016) A pivot-hinged, multilayer SU-8 micro motion amplifier assembled by a self-aligned approach. In: Micro Electro Mechanical Systems (MEMS), 2016 IEEE 29th International Conference on,pp. 75–78Google Scholar
  45. 45.
    Yao C, Bai X, Liu W, Latecki LJ (2014) Human detection using learned part alphabet and pose dictionary. In: European Conference on Computer Vision, pp. 251–266Google Scholar
  46. 46.
    Zeng Z-Q, Yu H-B, Xu H-R, Xie Y-Q, Gao J (2008) Fast training support vector machines using parallel sequential minimal optimization. 3rd International conference on In Intelligent System and Knowledge Engineering. ISKE 2008, pp. 997–1001Google Scholar
  47. 47.
    Zheng Y, Meng Y, Zhu Z (2008) Object detection and tracking using Bayes-constrained particle swarm optimization. In: Computer Vision Research Progress. Nova Science Publishers, Hauppauge, New York, pp. 1-16Google Scholar
  48. 48.
    Zhong Y, Jain AK, Dubuisson-Jolly M-P (2000) Object tracking using deformable templates. IEEE Trans Pattern Anal Mach Intell 22:544–549CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer ScienceAl-Balqa’ Applied University, Al-Huson University CollegeIrbidJordan
  2. 2.School of Computing, Science and EngineeringUniversity of Salford-ManchesterManchesterUK

Personalised recommendations