page_title: Features from accelerated segment test
section_title: Segment test detector
hierarchical_section_title: Features from accelerated segment test / Segment test detector
caption_attribution_description: English: An illustration of the pixels used by the FAST Corner Detection algorithm
context_page_description: Features from accelerated segment test is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. The most promising advantage of the FAST corner detector is its computational efficiency. Referring to its name, it is indeed faster than many other well-known feature extraction methods, such as difference of Gaussians used by the SIFT, SUSAN and Harris detectors. Moreover, when machine learning techniques are applied, superior performance in terms of computation time and resources can be realised. The FAST corner detector is very suitable for real-time video processing application because of this high-speed performance.
context_section_description: FAST corner detector uses a circle of 16 pixels (a Bresenham circle of radius 3) to classify whether a candidate point p is actually a corner. Each pixel in the circle is labeled from integer number 1 to 16 clockwise. If a set of N contiguous pixels in the circle are all brighter than the intensity of candidate pixel p (denoted by Iₚ) plus a threshold value t or all darker than the intensity of candidate pixel p minus threshold value t, then p is classified as corner.  The conditions can be written as:
  Condition 1: A set of N contiguous pixels S, , the intensity of x >  Iₚ + threshold, or 
  Condition 2: A set of N contiguous pixels S, , 
  So when either of the two conditions is met, candidate p can be classified as a corner. There is a tradeoff of choosing N, the number of contiguous pixels and the threshold value t. On one hand the number of detected corner points should not be too many, on the other hand, the high performance should not be achieved by sacrificing computational efficiency. Without the improvement of machine learning, N is usually chosen as 12.  A high-speed test method could be applied to exclude non-corner points.
caption: The pixels used by the FAST corner detector
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