Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine

Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The increasing risks of border intrusions or attacks on sensitive facilities and the growing availability of surveillance cameras lead to extensive research efforts for robust detection of pedestrians using images. However, the surveillance of borders or sensitive facilities poses many challenges including the need to set up many cameras to cover the whole area of interest, the high bandwidth requirements for data streaming and the high-processing requirements. Driven by day and night capabilities of the thermal sensors and the distinguished thermal signature of humans, the authors propose a novel and robust method for the detection of pedestrians using thermal images. The method is composed of three steps: a detection which is based on a saliency map in conjunction with a contrast-enhancement technique, a shape description based on discrete Chebyshev moments and a classification step using a support vector machine classifier. The performance of the method is tested using two different thermal datasets and is compared with the conventional maximally stable extremal regions detector. The obtained results prove the robustness and the superiority of the proposed framework in terms of true and false positives rates and computational costs which make it suitable for low-performance processing platforms and real-time applications.


    1. 1)
      • 1. Davis, J.W., Keck, M.A.: ‘A two-stage template approach to person detection in thermal imagery’. Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTIONS, 2005), Breckenridge, CO, USA, January 2005, vol. 1, pp. 364369.
    2. 2)
      • 2. Torabi, A., Massé, G., Bilodeau, G.-A.: ‘An iterative integrated framework for thermal-visible image registration, sensor fusion, and people tracking for video surveillance applications’, Comput. Vis. Image Underst., 2012, 116, pp. 210221.
    3. 3)
      • 3. Vandone, A.: ‘Algorithms for infrared image processing’, 2011.
    4. 4)
      • 4. Davis, J.W., Sharma, V.: ‘Background-subtraction using contour-based fusion of thermal and visible imagery’, Comput. Vis. Image Underst., 2007, 106, (2), pp. 162182.
    5. 5)
      • 5. Teutsch, M., Müller, T.: ‘Hot spot detection and classification in LWIR videos for person recognition’. SPIE Defense, Security, and Sensing, Baltimore, MA, USA, 2013, p. 87440F.
    6. 6)
      • 6. Teutsch, M., Müller, T., Huber, M., et al: ‘Low resolution person detection with a moving thermal infrared camera by hot spot classification’. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 2014, pp. 209216.
    7. 7)
      • 7. Davis, J., Sharma, V.: ‘Robust background-subtraction for person detection in thermal imagery’. IEEE Int. Workshop on Object Tracking and Classification beyond the Visible Spectrum, Washington, USA, 2004.
    8. 8)
      • 8. Freund, Y., Schapire, R.E.: ‘A decision-theoretic generalization of on-line learning and an application to boosting’. European Conf. Computational Learning Theory, Barcelona, Spain, 1995, pp. 2337.
    9. 9)
      • 9. Wang, W., Zhang, J., Shen, C.: ‘Improved human detection and classification in thermal images’. 2010 IEEE Int. Conf. Image Processing, Hong Kong, China, 2010, pp. 23132316.
    10. 10)
      • 10. Jungling, K., Arens, M.: ‘Feature based person detection beyond the visible spectrum’. 2009 IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, Miami, FL, USA, 2009, pp. 3037.
    11. 11)
      • 11. Rudol, P., Doherty, P.: ‘Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery’. 2008 IEEE Aerospace Conf., Big Sky, MT, USA, 2008, pp. 18.
    12. 12)
      • 12. Zhao, X., He, Z., Zhang, S., et al: ‘Robust pedestrian detection in thermal infrared imagery using a shape distribution histogram feature and modified sparse representation classification’, Pattern Recognit., 2015, 48, (6), pp. 19471960.
    13. 13)
      • 13. Yang, C., Liu, H., Liao, S., et al: ‘Pedestrian detection in thermal infrared image using extreme learning machine’. Proc. ELM-2014, Hangzhou, China, 2015, 2, pp. 3140.
    14. 14)
      • 14. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: ‘Extreme learning machine: theory and applications’, Neurocomputing, 2006, 70, (1), pp. 489501.
    15. 15)
      • 15. Ma, Y., Wu, X., Yu, G., et al: ‘Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery’, Sensors, 2016, 16, (4), p. 446.
    16. 16)
      • 16. Bouguet, J.-Y.: ‘Pyramidal implementation of the affine Lucas–Kanade feature tracker description of the algorithm’, Intel Corp., 2001, 5, (1-10), p. 4.
    17. 17)
      • 17. Achanta, R., Hemami, S., Estrada, F., et al: ‘Frequency-tuned salient region detection’. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA, 2009, pp. 15971604.
    18. 18)
      • 18. Arodź, T., Kurdziel, M., Popiela, T.J., et al: ‘Detection of clustered microcalcifications in small field digital mammography’, Comput. Methods Programs Biomed., 2006, 81, (1), pp. 5665.
    19. 19)
      • 19. Shu, H., Zhang, H., Chen, B., et al: ‘Fast computation of Tchebichef moments for binary and grayscale images’, IEEE Trans. Image Process., 2010, 19, (12), pp. 31713180.
    20. 20)
      • 20. Pozo, J.M., Villa-Uriol, M.-C., Frangi, A.F.: ‘Efficient 3d geometric and Zernike moments computation from unstructured surface meshes’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (3), pp. 471484.
    21. 21)
      • 21. Gao, X., Wang, Q., Li, X., et al: ‘Zernike-moment-based image super resolution’, IEEE Trans. Image Process., 2011, 20, (10), pp. 27382747.
    22. 22)
      • 22. Karakasis, E., Bampis, L., Amanatiadis, A., et al: ‘Digital elevation model fusion using spectral methods’. 2014 IEEE Int. Conf. Imaging Systems and Techniques (IST) Proc., Santorini, Greece, 2014, pp. 340345.
    23. 23)
      • 23. Karakasis, E.G., Papakostas, G.A., Koulouriotis, D.E., et al: ‘A unified methodology for computing accurate quaternion color moments and moment invariants’, IEEE Trans. Image Process., 2014, 23, (2), pp. 596611.
    24. 24)
      • 24. Matas, J., Chum, O., Urban, M., et al: ‘Robust wide-baseline stereo from maximally stable extremal regions’, Image Vis. Comput., 2004, 22, (10), pp. 761767.
    25. 25)
      • 25. St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: ‘A self-adjusting approach to change detection based on background word consensus’. 2015 IEEE Winter Conf. Applications of Computer Vision, Waikoloa, HI, USA, January 2015, pp. 990997.

Related content

This is a required field
Please enter a valid email address