Hubbry Logo
Features from accelerated segment testFeatures from accelerated segment testMain
Open search
Features from accelerated segment test
Community hub
Features from accelerated segment test
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Features from accelerated segment test
Features from accelerated segment test
Features from accelerated segment test
View on Wikipedia
from Wikipedia

Features from accelerated segment test (FAST) 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.[1] 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 (DoG) 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.

Segment test detector

[edit]
The pixels used by the FAST corner detector

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, , (a dark corner on a bright background)
  • Condition 2: A set of N contiguous pixels S, , (a bright corner on a dark background)

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.

High-speed test

[edit]

The high-speed test for rejecting non-corner points is operated by examining 4 example pixels, namely pixel 1, 9, 5 and 13. Because there should be at least 12 contiguous pixels that are whether all brighter or darker than the candidate corner, so there should be at least 3 pixels out of these 4 example pixels that are all brighter or darker than the candidate corner. Firstly pixels 1 and 9 are examined, if both I1 and I9 are within [Ip - t, Ip + t], then candidate p is not a corner. Otherwise pixels 5 and 13 are further examined to check whether three of them are brighter than Ip + t or darker than Ip - t. If there exists 3 of them that are either brighter or darker, the rest pixels are then examined for final conclusion. And according to the inventor in his first paper,[2] on average 3.8 pixels are needed to check for candidate corner pixel. Compared with 8.5 pixels for each candidate corner, 3.8 is really a great reduction which could highly improve the performance.

However, there are several weaknesses for this test method:

  1. The high-speed test cannot be generalized well for N < 12. If N < 12, it would be possible that a candidate p is a corner and only 2 out of 4 example test pixels are both brighter Ip + t or darker than Ip - t.
  2. The efficiency of the detector depends on the choice and ordering of these selected test pixels. However it is unlikely that the chosen pixels are optimal which take concerns about the distribution of corner appearances.
  3. Multiple features are detected adjacent to one another

Improvement with machine learning

[edit]

In order to address the first two weakness points of high-speed test, a machine learning approach is introduced to help improve the detecting algorithm. This machine learning approach operates in two stages. Firstly, corner detection with a given N is processed on a set of training images which are preferable from the target application domain. Corners are detected through the simplest implementation which literally extracts a ring of 16 pixels and compares the intensity values with an appropriate threshold.

For candidate p, each location on the circle x ∈ {1, 2, 3, ..., 16} can be denoted by p→x. The state of each pixel, Sp→x must be in one of the following three states:

  • d, Ip→x ≤ Ip - t (darker)
  • s, Ip - t ≤ Ip→x ≤ Ip + t (similar)
  • b, Ip→x≥ Ip + t (brighter)

Then choosing an x (same for all p) partitions P (the set of all pixels of all training images) into 3 different subsets, Pd, Ps, Pb where:

  • Pd = {p ∈ P : Sp→x = d }
  • Ps = {p ∈ P : Sp→x = s }
  • Pb = {p ∈ P : Sp→x = b }

Secondly, a decision tree algorithm, the ID3 algorithm is applied to the 16 locations in order to achieve the maximum information gain. Let Kp be a boolean variable which indicates whether p is a corner, then the entropy of Kp is used to measure the information of p being a corner. For a set of pixels Q, the total entropy of KQ (not normalized) is:

  • H(Q) = ( c + n ) log2( c + n ) - clog2c - nlog2n
    • where c = |{ i ∈ Q: Ki is true}| (number of corners)
    • where n = |{ i ∈ Q: Ki is false}| (number of non-corners)

The information gain can then be represented as:

  • Hg= H(P) - H(Pb) - H(Ps) - H(Pd)

A recursive process is applied to each subsets in order to select each x that could maximize the information gain. For example, at first an x is selected to partition P into Pd, Ps, Pb with the most information; then for each subset Pd, Ps, Pb, another y is selected to yield the most information gain (notice that the y could be the same as x ). This recursive process ends when the entropy is zero so that either all pixels in that subset are corners or non-corners.

This generated decision tree can then be converted into programming code, such as C and C++, which is just a bunch of nested if-else statements. For optimization purpose, profile-guided optimization is used to compile the code. The compiled code is used as corner detector later for other images.

Notice that the corners detected using this decision tree algorithm should be slightly different from the results using segment test detector. This is because that decision tree model depends on the training data, which could not cover all possible corners.

Non-maximum suppression

[edit]

"Since the segment test does not compute a corner response function, non-maximum suppression can not be applied directly to the resulting features." However, if N is fixed, for each pixel p the corner strength is defined as the maximum value of t that makes p a corner. Two approaches therefore could be used:

  • A binary search algorithm could be applied to find the biggest t for which p is still a corner. So each time a different t is set for the decision tree algorithm. When it manages to find the biggest t, that t could be regarded as the corner strength.
  • Another approach is an iteration scheme, where each time t is increased to the smallest value of which pass the test.

FAST-ER: Enhanced repeatability

[edit]

FAST-ER detector is an improvement of the FAST detector using a metaheuristic algorithm, in this case simulated annealing. So that after the optimization, the structure of the decision tree would be optimized and suitable for points with high repeatability. However, since simulated annealing is a metaheuristic algorithm, each time the algorithm would generate a different optimized decision tree. So it is better to take efficiently large amount of iterations to find a solution that is close to the real optimal. According to Rosten, it takes about 200 hours on a Pentium 4 at 3 GHz which is 100 repeats of 100,000 iterations to optimize the FAST detector.

Comparison with other detectors

[edit]

In Rosten's research,[3] FAST and FAST-ER detector are evaluated on several different datasets and compared with the DoG, Harris, Harris-Laplace, Shi-Tomasi, and SUSAN corner detectors.

The parameter settings for the detectors (other than FAST) are as follows:

Detector Parameter Setting Value
DoG
Scales per octave 3
Initial blur σ 0.8
Octaves 4
SUSAN Distance threshold 4.0
Harris, Shi-Tomasi Blur σ 2.5
Harris-Laplace Initial blur σ 0.8
Harris blur 3
Octaves 4
Scales per octave 10
General parameters ε 5 pixels
  • Repeatability test result is presented as the averaged area under repeatability curves for 0-2000 corners per frame over all datasets (except the additive noise):
Detector A
FAST-ER 1313.6
FAST-9 1304.57
DOG 1275.59
Shi & Tomasi 1219.08
Harris 1195.2
Harris-Laplace 1153.13
FAST-12 1121.53
SUSAN 1116.79
Random 271.73
  • Speed tests were performed on a 3.0 GHz Pentium 4-D computer. The dataset are divided into a training set and a test set. The training set consists of 101 monochrome images with a resolution of 992×668 pixels. The test set consists of 4968 frames of monochrome 352×288 video. And the result is:
Detector Training set pixel rate Test set pixel rate
FAST n=9 188 179
FAST n=12 158 154
Original FAST n=12 79 82.2
FAST-ER 75.4 67.5
SUSAN 12.3 13.6
Harris 8.05 7.90
Shi-Tomasi 6.50 6.50
DoG 4.72 5.10

References

[edit]

Bibliography

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Features from accelerated segment test

View on Grokipedia
from Grokipedia
Features from Accelerated Segment Test (FAST) is a high-speed corner detection in , designed to identify interest points or corners in images by examining the intensity differences of surrounding a candidate point using a test. Introduced by Edward Rosten and Tom Drummond in 2006, FAST employs techniques, specifically an classifier, to efficiently determine whether a qualifies as a corner, achieving real-time performance on live video streams while maintaining high repeatability across varying viewpoints and lighting conditions. The method outperforms traditional detectors like Harris and in speed, processing PAL video fields in under 2 milliseconds on contemporary hardware, making it suitable for applications such as object tracking, image matching, and (SLAM). The core of FAST involves testing a circle of 16 contiguous pixels around a central candidate pixel at a radius of 3, where a pixel is classified as a corner if there exists at least a contiguous segment of 12 (or a user-specified number n) pixels that are all brighter or darker than the candidate by a threshold value t. To accelerate detection, the algorithm uses a trained to minimize the number of intensity comparisons, typically requiring only about 2-3 pixel tests per candidate on average. Non-maximum suppression is applied post-detection to refine corner locations, ensuring distinct features. An improved version, FAST-9, sets the contiguous segment length n=9 for balancing speed and accuracy, demonstrating superior repeatability rates—up to 80% in 3D scene evaluations—compared to (DoG) and other methods under motion and illumination changes. Since its inception, FAST has been integrated into major libraries, including , where it serves as a foundational feature extractor for real-time systems. Its efficiency stems from avoiding computationally expensive calculations used in earlier approaches, instead relying on simple intensity thresholds and learned heuristics, though it may require additional descriptors like BRIEF for robust matching in complex scenarios. Evaluations on benchmark datasets, such as those involving 3D objects like boxes and reliefs, highlight FAST's robustness to noise and viewpoint shifts, positioning it as a cornerstone for embedded and mobile vision applications.

Introduction

History and Development

The Features from Accelerated Segment Test (FAST) corner detection originated from Edward Rosten's PhD research at the , supervised by Tom Drummond, with initial concepts developed during preliminary work in 2005. The full , incorporating for optimization, was formally proposed in the 2006 paper "Machine Learning for High-Speed Corner Detection," presented at the European Conference on Computer Vision (ECCV). This work built on an earlier non-machine learning prototype explored in Rosten's doctoral studies, focusing on a simple intensity threshold test around candidate pixels in a circular neighborhood to identify corners efficiently. The development of FAST was driven by the demand for real-time feature detection in and visual tracking applications, where traditional methods fell short. Earlier detectors, such as the introduced in 1988, relied on second-moment matrices derived from image gradients, offering robust but computationally expensive performance unsuitable for live on hardware of the early . Similarly, the (SIFT), proposed by David Lowe in 1999, provided scale-invariant keypoints through difference-of-Gaussians but required extensive computation, often exceeding real-time constraints for applications like (SLAM). FAST aimed to achieve high-speed detection—processing PAL video frames using less than 7% of available processing time—while maintaining comparable repeatability to these predecessors. Key milestones include the 2006 ECCV , which established FAST as a benchmark for speed in corner detection, and subsequent enhancements in the 2008 arXiv (published in IEEE Transactions on Pattern Analysis and Machine Intelligence in 2010) introducing FAST-ER. This refinement generalized the process using to prioritize repeatability across viewpoint changes, while preserving the core accelerated segment test mechanism. These advancements marked FAST's evolution from a speed-focused to a versatile, learning-based framework influential in real-time systems.

Overview and Basic Principles

The Features from Accelerated Segment Test (FAST) is a corner detection in that identifies interest points, or corners, in images by evaluating the intensity differences between a candidate and its surrounding neighbors. Developed by Edward Rosten and Tom Drummond, FAST operates on images and focuses on detecting pixels that exhibit significant local intensity variations, which are indicative of edges or structural changes useful for feature matching and tracking. At its core, FAST examines a discrete circle of 16 , derived from Bresenham's circle approximation, centered on a candidate pixel pp with intensity I(p)I(p). A corner is declared if there exists a contiguous arc of at least 12 pixels in this circle where each pixel's intensity differs from I(p)I(p) by more than a predefined threshold tt, meaning the arc is either sufficiently brighter (I(x)>I(p)+tI(x) > I(p) + t) or darker (I(x)<I(p)−tI(x) < I(p) - t) than the center. This threshold-based comparison avoids computing gradients or derivatives, relying instead on simple intensity thresholding to classify the pixel. The primary motivation for FAST lies in its exceptional computational efficiency, enabling real-time processing of video streams without the overhead of multi-scale analysis or complex feature descriptors found in methods like Harris or Difference of Gaussians. By prioritizing speed—achieving detection in under 7% of available processing time on standard hardware for PAL video—FAST balances detection quality with performance demands, making it suitable for applications such as simultaneous localization and mapping (SLAM), object tracking, and augmented reality.

Core Algorithm

Segment Test Mechanism

The segment test mechanism in the Features from Accelerated Segment Test (FAST) algorithm begins by considering a candidate pixel pp with intensity IpI_p. A circle of 16 pixels is sampled around pp at a radius of 3, corresponding to positions numbered 1 through 16 along the circumference. For each surrounding pixel at position xx, its intensity Ip→xI_{p \to x} is classified relative to IpI_p using a user-defined threshold tt: the pixel is deemed brighter if Ip→x≥Ip+tI_{p \to x} \geq I_p + t, darker if Ip→x≤Ip−tI_{p \to x} \leq I_p - t, or similar otherwise. This threshold test identifies pixels that exhibit a sufficient intensity difference from the candidate, enabling the detection of abrupt changes characteristic of corners. A candidate pixel pp is classified as a corner if there exists at least one contiguous arc of 12 or more pixels (out of the 16) that are all brighter than or equal to Ip+tI_p + t or all darker than or equal to Ip−tI_p - t. Exact matches to the threshold boundaries are incorporated into the brighter or darker categories due to the inclusive inequalities, ensuring that borderline intensity differences contribute to arc formation rather than being excluded. The basic segment test provides no inherent invariance to rotation or scale, as it relies solely on fixed circumferential sampling without additional transformations. The brute-force implementation of the segment test, used for initial corner detection and training data generation, iterates over all possible starting positions in the circle for each candidate pixel. This involves checking each of the 16 potential arcs of length 12 (wrapping around the circle if necessary) to verify continuity in the brighter or darker condition.

pseudocode

for each candidate pixel p: for s = 1 to 16: # starting position in circle all_brighter = true all_darker = true for i = 0 to 11: # check 12 contiguous pixels x = (s + i - 1) mod 16 + 1 if I[p → x] < I_p + t: all_brighter = false if I[p → x] > I_p - t: all_darker = false if all_brighter or all_darker: classify p as corner break

for each candidate pixel p: for s = 1 to 16: # starting position in circle all_brighter = true all_darker = true for i = 0 to 11: # check 12 contiguous pixels x = (s + i - 1) mod 16 + 1 if I[p → x] < I_p + t: all_brighter = false if I[p → x] > I_p - t: all_darker = false if all_brighter or all_darker: classify p as corner break

This exhaustive approach confirms the presence of a qualifying contiguous segment but is computationally intensive for real-time applications.

High-Speed Testing Procedure

The high-speed testing procedure in the Features from Accelerated Segment Test (FAST) algorithm employs a fixed sequential evaluation of pixels on a 16-point Bresenham circle surrounding the candidate pixel pp with intensity IpI_p, using a threshold tt to determine intensity differences. This approach begins by examining pixels 1 and 9, which are positioned opposite each other across the circle from pp. If both pixels 1 and 9 have intensities within tt of IpI_p (i.e., neither is sufficiently brighter than or equal to Ip+tI_p + t nor darker than or equal to Ip−tI_p - t), the candidate pp is immediately rejected as a non-corner, as such uniformity suggests a lack of corner-like contrast. This initial check leverages empirical observations that corners typically exhibit strong intensity variations in opposing directions, allowing rapid dismissal of the majority of non-corner pixels without further computation. If at least one of pixels 1 or 9 differs from IpI_p by at least tt, the procedure advances to test 5 and 13, which lie at 90-degree offsets (the cardinal directions). The candidate proceeds to the full segment test only if at least three of the four tested (1, 5, 9, and 13) consistently differ from IpI_p in the same direction—either all brighter than or equal to Ip+tI_p + t or all darker than or equal to Ip−tI_p - t—indicating potential corner continuity. Otherwise, pp is rejected. This branching logic ensures that exhaustive examination of the entire circle occurs only for a small fraction of candidates, with most non-corners eliminated after just two to four comparisons. The fixed order of testing, derived from correlations in responses observed in data, prioritizes high-information to maximize early rejection rates. This design achieves significant efficiency, rejecting most non-corner pixels after only 1–3 tests and resulting in an average of approximately 2.8 pixel evaluations per candidate across an image. On early hardware such as a 2.6 GHz processor, the original FAST implementation processes PAL video fields (768 × 288 pixels), detecting around 500 features per field in under 1.6 milliseconds—less than 8% of the available processing time per frame. Such performance enabled real-time operation for applications like tracking and SLAM. However, the procedure trades some detection accuracy and noise robustness for speed, as the limited pixel sampling reduces the averaging of intensity variations and may overlook subtle corners.

Machine Learning Optimization

The optimization in the Features from Accelerated Segment Test (FAST) employs the algorithm to learn an efficient sequence for testing the 16 neighboring around a candidate corner , thereby accelerating the process while maintaining accuracy. This approach addresses the inefficiency of testing all neighbors in a fixed or random order by prioritizing tests that quickly eliminate non-corner , which form the vast majority of candidates. Training begins with a set of natural images selected from the target application domain, such as those used in real-time tracking or augmented reality. For each pixel in these images, the ground truth label (corner or non-corner) is determined using the full segment test criterion via a brute-force evaluation that examines all 16 circle positions, resulting in a labeled dataset comprising all pixels across the training images. The feature vector for a candidate pixel pp consists of 16 ternary values for its neighbors ii, representing their states relative to IpI_p and tt: darker if I(i)≤Ip−tI(i) \leq I_p - t, similar if Ip−t<I(i)<Ip+tI_p - t < I(i) < I_p + t, and brighter if I(i)≥Ip+tI(i) \geq I_p + t, where II denotes image intensity. The ID3 algorithm constructs the decision tree by recursively selecting the neighbor position that maximizes information gain—defined as the reduction in entropy—splitting the data into three branches per test: the neighbor brighter than or equal to, darker than or equal to, or similar to the center pixel. The tree learns an optimal branching sequence, for example starting with the pixel opposite the center (position 9), followed by positions such as 3 and 15, to minimize the number of tests required for non-corners. Construction continues until subsets have zero entropy, yielding a tree with variable depth but typically averaging 2–3 tests per pixel, as non-corners are resolved early. Classification is performed by traversing the decision tree based on the ternary states of the neighboring pixels, reaching a leaf that classifies the candidate as a corner or non-corner, approximating the segment test criterion learned during training. This optimization reduces the average tests per pixel from 2.8 in the fixed-sequence baseline to 2.26 for n=9n=9 and 2.39 for n=12n=12, enabling up to a twofold speedup in overall detection time and allowing full processing of live PAL video (768×288 pixels) in under 7% of available CPU power on contemporary hardware. The method demonstrates robustness when evaluated on datasets including affine transformations of natural scenes, though training uses untransformed images. Open-source code for training the decision tree is provided through associated libraries.

Post-Processing and Enhancements

Non-Maximum Suppression

After detecting corner candidates using the FAST algorithm, each candidate is assigned a score VV, defined as the maximum over the brighter and darker cases of the sum of ∣I(x)−I(p)∣−t|I(x) - I(p)| - t for all pixels xx in the 16-pixel circle that exceed the intensity threshold tt. This score quantifies the strength of the corner response by measuring the total intensity variation of qualifying pixels. Non-maximum suppression (NMS) is then applied to thin the set of candidates, retaining only those with locally maximal scores. For each candidate, its score is compared to those of neighboring candidates within a 3×3 window; any candidate whose score is not the maximum in this neighborhood is suppressed and discarded. This process eliminates duplicate detections clustered around the same corner location. To implement NMS efficiently without resorting to O(n2)O(n^2) pairwise comparisons across all candidates, the candidates are typically sorted by score and local maxima are identified. This reduces the number of retained features by eliminating clustered detections, depending on image content and density of corners. The rationale for NMS in FAST stems from the algorithm's tendency to identify multiple adjacent pixels within thick corner structures due to its segment-based test. By enforcing a single maximal response per corner, NMS improves feature quality and distinctiveness, which is essential for downstream tasks like feature matching and tracking.

FAST-ER for Repeatability

The FAST-ER (Efficient Repeatability) variant of the FAST corner detector addresses limitations in repeatability across viewpoint and illumination changes by optimizing the detection process through machine learning techniques tailored for robustness. It extends the standard FAST algorithm by training a classifier that selects corners based on their consistency under simulated transformations, thereby enhancing matching performance in real-world scenarios without significantly sacrificing speed. Central to FAST-ER is the use of multiple intensity thresholds during feature classification to better distinguish corner candidates. Specifically, the algorithm applies thresholds such as tt, 1.5t1.5t, and 2t2t relative to the center pixel intensity to categorize surrounding pixels as darker, similar, or brighter, enabling a more nuanced decision tree that improves detection accuracy. To further boost invariance, FAST-ER incorporates resampling during training: images are subjected to weak affine simulations, including averaging features across small scale variations and applying transformations like rotations, reflections, and intensity inversions (typically 16 variants). This training process employs a boosted classifier optimized via simulated annealing on resampled image pairs, focusing on datasets that mimic common distortions such as viewpoint shifts and lighting variations. The repeatability of detected features is quantified using the score R=NrepeatedNusefulR = \frac{N_{\text{repeated}}}{N_{\text{useful}}}, where NrepeatedN_{\text{repeated}} is the number of matching features between image pairs, and NusefulN_{\text{useful}} represents the total potentially visible features in the overlapping regions. Features are selected if their scores exceed a learned threshold, ensuring only robust corners are retained. This approach yields a key improvement of 20-30% in repeatability over the standard FAST-9 variant, particularly under viewpoint and illumination changes, while incurring only a minor computational overhead due to the efficient decision tree structure (approximately 30,000 nodes). Despite these advances, FAST-ER remains limited in scale invariance, as it does not inherently handle large scale differences; it is typically combined with an image pyramid for multi-scale detection to address this.

Modern Variants and Improvements

Since the introduction of the original FAST algorithm, several variants have emerged to address limitations in orientation estimation, computational efficiency across scales, and hardware acceleration, particularly in resource-constrained environments. One prominent extension is Oriented FAST (oFAST), which enhances the standard FAST by incorporating orientation estimation for detected corners using intensity centroid moments within the local patch around each candidate point. This addition improves rotational invariance without significantly increasing computational overhead, making it suitable for real-time applications like feature matching. oFAST was integrated into the ORB (Oriented FAST and Rotated BRIEF) framework, where it serves as the keypoint detector paired with a rotation-invariant binary descriptor. To further optimize speed and adaptability, the Features from Adaptive Accelerated Segment Test (FAAST) detector modifies the FAST mechanism by dynamically adjusting arc lengths (ranging from 9 to 12 pixels) and mask sizes across different scales in a pyramid representation. This adaptive approach reduces processing time by up to 30% compared to fixed-parameter FAST while maintaining comparable corner detection quality, as evaluated on standard datasets like Oxford Affine Regions. FAAST prioritizes efficiency in multi-scale feature extraction, enabling faster pyramid construction for applications requiring scale invariance. Hardware accelerations have also advanced FAST's deployment on parallel architectures. A 2025 study proposed a hybrid parallel implementation combining FAST for initial corner detection with Harris scoring on low-end embedded GPUs, achieving up to 7.3x speedup compared to OpenCV GPU implementations on devices like NVIDIA Jetson TX2 through optimized binary encoding and thread-level parallelism. Similarly, NVIDIA's Vision Programming Interface (VPI) library, updated in 2025, provides CUDA-optimized FAST implementations that leverage GPU tensor cores for corner detection, offering improved performance on high-end GPUs while supporting seamless integration in vision pipelines. Other improvements include integrations like BRISK, which employs FAST for keypoint detection alongside a binary scale-space descriptor to enhance robustness to scale and rotation changes, outperforming SIFT in speed for wide-baseline matching. Adaptive thresholding variants adjust the intensity offset dynamically based on local image statistics, thereby improving detection reliability under varying illumination conditions without global parameter tuning. Recent trends emphasize hybrid systems where FAST's lightweight core is combined with deep learning components for end-to-end feature detection, such as using FAST-extracted keypoints to initialize convolutional networks for refined localization in object recognition tasks. Despite these integrations, FAST variants retain their appeal in embedded systems due to sub-millisecond detection speeds on low-power hardware, contrasting with the higher latency of fully learned detectors.

Performance and Evaluation

Speed and Computational Efficiency

The FAST corner detector achieves high computational efficiency primarily through its design, which performs an average of O(1) operations per pixel by employing early rejection mechanisms during the segment test. This involves initially checking only four pixels in a Bresenham circle around a candidate pixel and aborting further tests if they do not meet the intensity threshold criteria, thereby avoiding full evaluations for most non-corner pixels. Additionally, the algorithm relies solely on integer comparisons without any floating-point operations, minimizing computational overhead and enabling straightforward optimization on various hardware platforms. In its original 2006 implementation, FAST demonstrated impressive runtime performance, processing grayscale images at speeds equivalent to detecting hundreds of corners per millisecond on contemporary CPUs; for instance, the learned variant (with n=12 and non-maximum suppression) took approximately 4.6 ms per image on an 850 MHz Pentium III processor. On a faster 2.6 GHz Opteron, this reduced to about 1.34 ms, allowing real-time processing of PAL video (768×288 resolution) using less than 7% of the frame budget. These benchmarks highlight FAST's suitability for high-speed applications even on early-2000s hardware. Modern implementations further amplify these advantages through hardware acceleration. For example, NVIDIA's Vision Programming Interface (VPI) version 2.0, released in 2022, processes 1080p (1920×1080) images in under 1 ms (0.425 ms) on Jetson AGX Orin GPUs using , achieving over 2,300 frames per second and up to 45x speedup compared to 's CUDA backend or 5x over its CPU version. This represents a 10x or greater real-time speedup for video applications on mobile GPUs, making FAST viable for embedded systems like robotics and autonomous vehicles. Relative to baseline detectors, FAST offers substantial speed gains, particularly on grayscale images from the Oxford affine covariant regions dataset. The 2008 refined version (FAST-9) processed at 188 megapixels per second on a 3.0 GHz Pentium 4, outperforming by approximately 23x ( at ~8 MPix/s) and (via Difference-of-Gaussians) by over 40x ( at ~5 MPix/s), while using only 5% of a 640×480 video frame budget compared to 's 115%. These comparisons underscore FAST's efficiency edge without sacrificing too much on detection quality. FAST's architecture scales well with vectorized instructions, benefiting from SIMD extensions such as SSE and AVX in optimized libraries like , where assembly-accelerated routines process multiple pixels in parallel to boost throughput on x86 CPUs. GPU variants, including those in VPI, leverage massive parallelism to handle 1080p frames at 100+ FPS even under conservative settings, with potential for thousands of FPS in low-latency configurations.

Repeatability and Robustness

The repeatability of the FAST feature detector is evaluated using the percentage of detected features that match between a reference image and a transformed version, where matches are determined within a small spatial tolerance (typically 1.5 pixels) and overlap threshold (e.g., 80% for affine covariant regions). This metric is commonly assessed on the Oxford affine covariant regions dataset, which includes sequences simulating viewpoint changes, scale variations, blur, and illumination shifts. The basic FAST detector achieves approximately 50-60% repeatability under moderate transformations such as 30° rotations and scale changes up to 2x, as observed on challenging sequences like bas-relief with non-affine warps. However, performance drops significantly under blur or illumination changes, often falling below 40% in sequences with Gaussian blur (σ > 2) or varying lighting conditions. The FAST-ER variant, optimized via for repeatability, improves this to up to 80% under moderate viewpoint and scale changes, as demonstrated on the (viewpoint/rotation) and (scale) sequences from the in evaluations with 500-2000 features per frame. Key robustness factors include threshold tuning, where higher thresholds reduce false positives in noisy images but may miss weak corners, enhancing stability under (σ < 5). Limitations persist in low-contrast regions or highly textured areas, where the intensity-based segment test struggles to distinguish corners from edges or uniform patches.

Comparisons with Other Detectors

The Features from Accelerated Segment Test (FAST) detector offers significant advantages in computational speed over the due to its avoidance of eigenvalue computations in the second-moment matrix, enabling real-time performance on resource-constrained devices. However, FAST is less robust to rotation compared to Harris, as it relies on intensity comparisons along a discrete circle without inherent orientation estimation, potentially leading to lower under significant viewpoint changes. Harris, while slower, provides better sub-pixel accuracy through its continuous corner response function, making it preferable for applications requiring precise localization. In contrast to (SIFT), FAST achieves approximately 40 times greater speed on standard benchmarks, processing images at rates far exceeding SIFT's capabilities for real-time tasks. Repeatability under affine transformations is comparable between FAST and SIFT in controlled planar scenes, but FAST lacks built-in and descriptor generation, limiting its utility for robust matching. SIFT excels in wide-baseline stereo matching and due to its multi-scale difference-of-Gaussians detection and gradient-based descriptors, which provide superior invariance to scale and illumination changes at the cost of higher computational demands. Modern binary feature methods like (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK) build directly on FAST's core detection mechanism, addressing its limitations in rotation invariance. ORB enhances FAST by adding an intensity-weighted for orientation estimation and pairs it with a rotated BRIEF descriptor, achieving similar speeds while improving robustness to rotations and noise without sacrificing much efficiency. BRISK extends FAST with a pyramid and sampling pattern for , offering better performance in multi-scale scenarios at a modest speed penalty compared to plain FAST. Both ORB and BRISK maintain FAST's high feature detection rates, making them suitable hybrids for descriptor-inclusive pipelines.
DetectorSpeed (ms per image, approx.)Avg. # FeaturesKey Trade-off
FAST5-101000-2000Fastest detection; limited invariance
Harris100-120500-1000Accurate but slow; good for precision
SIFT190-200200-500Robust matching; scale-invariant but slow
ORB15-20800-1500Rotation-invariant FAST variant; balanced
BRISK20-30600-1200Scale-aware; slightly slower than ORB
Table notes: Speed from HPSequences benchmark on standard hardware; feature counts vary by content. FAST outperforms on speed for embedded systems, while SIFT and Harris suit precision-critical tasks. FAST is ideally suited for real-time applications on mobile or embedded platforms where speed is paramount, whereas Harris, SIFT, ORB, and BRISK are preferred for scenarios demanding higher invariance and matching accuracy.

Applications and Implementations

Key Use Cases in Computer Vision

The Features from Accelerated Segment Test (FAST) detector excels in real-time computer vision applications due to its computational efficiency, enabling robust feature detection in resource-constrained environments. One prominent use case is real-time tracking in simultaneous localization and mapping (SLAM) systems for robotics, where FAST identifies keypoints for keyframe selection and pose estimation. For instance, the Parallel Tracking and Mapping (PTAM) system, introduced in 2007, employs the FAST-10 corner detector across image pyramids to initialize and maintain tracks in monocular setups, achieving stable mapping in dynamic scenes without non-maximum suppression during tracking for speed. This integration allows PTAM to operate at video rates, supporting applications like robotic navigation in unstructured environments. In (AR), FAST facilitates feature matching for camera relocalization and scene understanding on mobile devices, where low-latency processing is essential for immersive experiences. Systems leveraging natural features, such as wide-area AR frameworks, use FAST to detect up to 500 keypoints per frame for rapid pose recovery, outperforming slower detectors in dynamic lighting conditions typical of handheld AR. Although marker-based libraries like primarily rely on fiducial tracking, extensions and hybrid integrations incorporate FAST-derived features (e.g., via ORB descriptors) for enhanced natural feature handling in mobile AR apps. Similarly, in for autonomous vehicles, FAST hybrids contribute to trajectory estimation on benchmarks like KITTI, where oriented variants enable efficient point tracking across stereo frames, reducing drift in high-speed driving scenarios. For , evaluations show FAST features yielding comparable accuracy to Harris corners in face reconstruction workflows, with superior speed for iterative refinement in multi-view setups. In 2025, FAST-based methods, such as those accelerated on low-power microcontrollers via ORB-SLAM variants, enable drone navigation on edge devices, supporting onboard visual-inertial for obstacle avoidance and mapping in resource-limited UAVs. A seminal from Rosten's 2006 work demonstrates FAST's efficacy in tracking, processing live video at over 30 frames per second on contemporary hardware for applications like real-time annotations, highlighting its role in enabling high-speed, drift-free pose estimation without sacrificing .

Software Libraries and Modern Deployments

The FAST corner detection algorithm is implemented in several prominent open-source libraries, enabling its widespread use in various applications. In , the cv::FastFeatureDetector class has been available since version 2.1 released in 2009, providing efficient corner detection with configurable thresholds and support for loading pre-trained decision trees to classify corners. This implementation allows for both non-maximum suppression and the FAST-ER variant, making it suitable for real-time processing in C++, Python, and other bindings. Other libraries extend FAST's accessibility across programming languages. BoofCV, a Java-based open-source library, includes a FAST corner detector that supports both the original and accelerated variants, optimized for performance in image processing pipelines. VLFeat, a C library focused on visual features, incorporates FAST along with the FAST-ER extension for improved repeatability, offering and C interfaces for academic and research use. More recently, NVIDIA's Vision Programming Interface (VPI) introduced CUDA-accelerated FAST detection in its 2025 release for Jetson platforms, enabling high-throughput corner extraction on edge devices with GPU support. Community-driven repositories on provide additional implementations and bindings. The original FAST codebase by Edward Rosten is hosted as a in C, allowing developers to build and customize the algorithm from source. Python bindings are available through repositories like welinder/pyfast, which wraps the core FAST functionality for seamless integration into NumPy-based workflows and scripting environments. In modern deployments, FAST is integrated into robotics and embedded systems frameworks. The 2 (ROS2) utilizes FAST via its vision_opencv package for real-time feature detection in navigation and SLAM tasks on autonomous robots. For mobile and edge AI, hybrid approaches combine FAST with in TensorFlow Lite, where classical FAST preprocessing accelerates keypoint detection before neural network refinement, as seen in 2025 optimizations for resource-constrained devices. GPU accelerations for FAST in embedded AI contexts, including variants, have gained traction in 2025 for applications like drone vision and AR/VR, reducing latency in feature extraction pipelines. Pre-trained decision trees for FAST are readily available for both grayscale and color images, distributed through libraries like to avoid retraining overhead. Extensions such as ORB in leverage FAST as the underlying keypoint detector, combining it with binary descriptors for robust feature matching in modern pipelines.
Add your contribution
Related Hubs
User Avatar
No comments yet.