Robustness of Point Feature Detection

  • Zijiang Song
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)


This paper evaluates 2D feature detection methods with respect to invariance and efficiency properties. The studied feature detection methods are as follows: Speeded Up Robust Features, Scale Invariant Feature Transform, Binary Robust Invariant Scalable Keypoints, Oriented Binary Robust Independent Elementary Features, Features from Accelerated Segment Test, Maximally Stable Extremal Regions, Binary Robust Independent Elementary Features, and Fast Retina Keypoint. A long video sequence of traffic scenes is used for testing these feature detection methods. A brute-force matcher and Random Sample Consensus are used in order to analyse how robust these feature detection methods are with respect to scale, rotation, blurring, or brightness changes. After identifying matches in subsequent frames, RANSAC is used for removing inconsistent matches; remaining matches are taken as correct matches. This is the essence of our proposed evaluation technique. All the experiments use a proposed repeatability measure, defined as the ratio of the numbers of correct matches, and of all keypoints.


Feature Detection Scale Invariant Feature Transform Correct Match Maximally Stable Extremal Region Random Sample Consensus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zijiang Song
    • 1
  • Reinhard Klette
    • 1
  1. 1.The .enpeda.. Project, Department of Computer ScienceThe University of AucklandNew Zealand

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