Recursive RANSAC: Multiple Signal Estimation with Outliers

Niedfeldt, Peter C.; Beard, Randal W.

doi:10.3182/20130904-3-fr-2041.00213

Cited by 24 publications

(8 citation statements)

References 15 publications

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“…In order to assess the proposed method, it is compared with the Hough transform by applying the algorithm of Sanchez found in the MATLAB central [ 25 ] and by running the Hough transform for parabolic shapes algorithm provided by the MIPAV [ 26 ] software, that can be downloaded from the website [ 37 ]. However, considering that MIPAV and MATLAB parabola detection implementations are based on Hough transform, RANSAC algorithm [ 21 ] was implemented to analyze the performance of the proposed algorithm (cf. Tables 3 and 4 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fast Parabola Detection Using Estimation of Distribution Algorithms

Guerrero-Turrubiates

Cruz-Aceves

Ledesma

et al. 2017

Computational and Mathematical Methods in Medicine

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This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

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Section: Resultsmentioning

confidence: 99%

“…The convergence is not warranted because initialization is chosen randomly from a small data subset (i.e., results are not repeatable). In some special cases, RANSAC is not always capable of obtaining the optimal results for well-conditioned data [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Parabola Detection Using Estimation of Distribution Algorithms

Guerrero-Turrubiates

Cruz-Aceves

Ledesma

et al. 2017

Computational and Mathematical Methods in Medicine

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“…In our implementation, the tracking UAS and the handoff UAS both visually track the ground targets using the R-RANSAC-based visual multiple target tracking (VMTT) algorithm, originally presented in [12], and extended in [13], [14], [15], [16] to tracking from multi-rotor aircraft. In this paper we extend the algorithm to fixed-wing aircraft.…”

Section: Multiple Target Trackingmentioning

confidence: 99%

Robust Moving Target Handoff in GPS-Denied Environments

Beard¹,

Tolman²,

Morris³

et al. 2021

Preprint

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Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited ﬂight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary ﬁlter for self-pose estimation using only an IMU, a particle ﬁlter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the ﬁnal target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

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“…This is a problem where object detections have to be associated with objects already being tracked, or as a novel object entering the image frame. Niedfeldt and Beard (2013) propose a multiple object tracking framework which performs tracking and data association simultaneously based on the Random Sample Consensus (RANSAC) algorithm. RANSAC is a powerful algorithm used in many machine vision problems (Niedfeldt & Beard, 2013), and is based on the concept that a data set contains so‐called “inliers” and “outliers”.…”

Section: Introductionmentioning

confidence: 99%

“…Typically algorithms would estimate the model parameters using the total data set; RANSAC, however, assumes that given a small set of inlier data points, there exists a procedure which can estimate the parameters of a model that optimally explains or fits the data. Niedfeldt and Beard (2013) and Niedfeldt and Beard (2014) present how this can be a powerful tool when performing multiple object tracking of an unknown number of dynamic objects. The strength of the approach is in its ability to handle a huge amount of false positives and false negatives from the object detection module.…”

Section: Introductionmentioning

confidence: 99%

Object detection, recognition, and tracking from UAVs using a thermal camera

Leira

Helgesen

Johansen

et al. 2020

Journal of Field Robotics

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In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

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Recursive RANSAC: Multiple Signal Estimation with Outliers

Cited by 24 publications

References 15 publications

Fast Parabola Detection Using Estimation of Distribution Algorithms

Fast Parabola Detection Using Estimation of Distribution Algorithms

Robust Moving Target Handoff in GPS-Denied Environments

Object detection, recognition, and tracking from UAVs using a thermal camera

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