Performance Analysis of Feature Detection and Description (FDD) Methods on Accident Images

  • P. Joyce Beryl Princess
  • Salaja SilasEmail author
  • Elijah Blessing Rajsingh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)


Feature detection and description is a significant process in several computer vision tasks such as object recognition, detection, image classification and registration. The challenge relies on choosing appropriate feature detector and descriptor, concerning an application, regardless of the appearance and the content of an image. The objective of the performance analysis is to identify the suitable FDD method, in order to extract distinct and robust features from the accident images, possess crucial information for the analysis of accident severity. In this paper, the feature detection and description methods from the literature is applied to the accident images. However, the accident images captured in real-time through mobile cameras confronts motion blur, illumination, rotation and scale variations. Therefore, the robustness of the feature detectors and descriptors are evaluated under various image transformations and the results are compared and analyzed. The result shows, under feature detectors, CenSurE and SIFT performs better with reference to repeatability and matching score. SIFT and ORB are better in the category of feature descriptors for the analysis of accident images.


Feature detector Feature descriptor Repeatability Matching score SIFT MSER SURF FAST CenSurE AGAST ORB BRISK BRIEF FREAK 


  1. 1.
    Mahata, D., Narzary, P.K., Govil, D.: Spatio-temporal analysis of road traffic accidents in Indian large cities. Clin. Epidemiol. Glob. Health 7(4), 586–591 (2019)CrossRefGoogle Scholar
  2. 2.
    Cadoni, M., Lagorio, A., Grosso, E.: Incremental models based on feature persistence for object recognition. Pattern Recognit. Lett. 122, 38–44 (2019)CrossRefGoogle Scholar
  3. 3.
    Bhuvaneswari, R., Subban, R.: Novel object detection and recognition system based on points of interest selection and SVM classification. Cogn. Syst. Res. 52, 985–994 (2018)CrossRefGoogle Scholar
  4. 4.
    Xu, W., Zhong, S., Yan, L., Wu, F., Zhang, W.: Moving object detection in aerial infrared images with registration accuracy prediction and feature points selection. Infrared Phys. Technol. 92, 318–326 (2018)CrossRefGoogle Scholar
  5. 5.
    Vinay, A., Aklecha, N., Meghana, Murthy, K.N.B., Natarajan, S.: On detectors and descriptors based techniques for face recognition. Procedia Comput. Sci. 132, 908–917 (2018)Google Scholar
  6. 6.
    Awad, A.I., Hassaballah, M.: Image feature detectors and descriptors: foundations and applications. Stud. Comput. Intell. 630, 11–46 (2016)Google Scholar
  7. 7.
    Mouats, T., Aouf, N., Nam, D., Vidas, S.: Performance evaluation of feature detectors and descriptors beyond visible. J. Intell. Robot. Syst. 92, 33–63 (2018). Scholar
  8. 8.
    Lee, M., Park, I.: Performance evaluation of local descriptors for maximally stable extremal regions. J. Vis. Commun. Image Represent. 47, 62–72 (2017)CrossRefGoogle Scholar
  9. 9.
    Malekabadi, A., Khojastehpour, M., Emadi, B.: A comparative evaluation of combined feature detectors and descriptors in different color spaces for stereo image matching of tree. Scientia Horticulturae 228, 187–195 (2018)CrossRefGoogle Scholar
  10. 10.
    Wu, S., Oerlemans, A., Bakker, E.M., Lew, S.: A comprehensive evaluation of local detectors and descriptors. Sig. Process. Image Commun. 59, 150–167 (2017)CrossRefGoogle Scholar
  11. 11.
    Hietanen, A., Lankinen, J., Kristian, J., Glent, A., Krüger, N.: A comparison of feature detectors and descriptors for object class matching. Neurocomputing 184, 3–12 (2016)CrossRefGoogle Scholar
  12. 12.
    Loncomilla, P., Ruiz-del-Solar, J.: Object recognition using local invariant features for robotic applications: a survey. Pattern Recogn. 60, 499–514 (2016)CrossRefGoogle Scholar
  13. 13.
    Johansson, J.: Interest point detectors and descriptors for IR images: an evaluation of common detectors and descriptors on IR images, Dissertation (2015)Google Scholar
  14. 14.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  15. 15.
    Shi, J., Tomasi, C.: Good features to track. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593–600 (1994)Google Scholar
  16. 16.
    Rosten, E., Drummond, T.: Fusing points and lines for high performance real-time tracking. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV 2005) (2005)Google Scholar
  17. 17.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). Scholar
  18. 18.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010). Scholar
  19. 19.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)CrossRefGoogle Scholar
  20. 20.
    Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: Center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008). Scholar
  21. 21.
    Brisk, B., Card, O.R.B.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2011)Google Scholar
  22. 22.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)Google Scholar
  23. 23.
    Alahi, A., Ortiz, R., Vandergheynst P.: FREAK: fast retina keypoint. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)Google Scholar
  24. 24.
    Zhang, F., Ye, F., Su, Z.: A modified feature point descriptor based on binary robust independent elementary features. In: Proceedings of 2014 7th International Congress on Image and Signal Processing, CISP 2014, pp. 258–263(2014)Google Scholar
  25. 25.
    Lowe, D.G.: Distinctive image features from sclae-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). Scholar
  26. 26.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up robust features (SURF)”. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  27. 27.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  28. 28.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000). Scholar
  29. 29.
    Road Accident Data – India.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Karunya Institute of Technology and SciencesCoimbatoreIndia

Personalised recommendations