A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

Jalloul, Manal K.; Al-Alaoui, Mohamad Adnan

doi:10.1016/j.image.2015.09.010

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…From (8), one can see that once the epipoles are e 0 v ¼ a v 4 and e 00 w ¼ b w 4 , the trifocal tensor can be recovered linearly by rectifying the remaining entries a v 4 and b w 4 . This relationship can be parametrised linearly as t ¼ Ea, where t is a vector containing the tensor parameters, E is a matrix representing the linear relationship in (8) and a is the vector of the remaining entries a v 4 and b w 4 . This is performed by minimising kAEak where AE k k ¼ 1: The matrix A is constructed over the transferred image point using the estimated trifocal tensor.…”

Section: Trifocal Tensor Estimation Using Convex Optimisationmentioning

confidence: 99%

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Robust L_∞ convex optimisation for UAVs cooperative motion estimation

Boulekchour

Aouf

Richardson

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, a system for real-time cooperative monocular visual motion estimation with multiple unmanned aerial vehicles is proposed. Distributing the system across a network of vehicles allows for efficient processing in terms of both computational time and estimation accuracy. The resulting global cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms are developed within a convex optimisation framework on-board the monocular vision systems of each vehicle. In the presented novel distributed cooperative strategy a visual loop-closure module is deployed to detect any simultaneously overlapping fields of view of two or more of the vehicles. A positive feedback from the latter module triggers the collaborative motion estimation algorithm between any vehicles involved in this loop-closure. This scenario creates a flexible stereo set-up which jointly optimises the motion estimates of all vehicles in the cooperative scheme. Prior to that, vehicle-to-vehicle relative pose estimates are recovered with a novel robust registration solution in a global optimisation framework. Furthermore, as a complementary solution, a robust non-linear H∞filter is designed to fuse measurements from the vehicles’ on-board inertial sensors with the visual estimates. The proposed cooperative navigation solution has been validated on real-world data, using two unmanned aerial vehicles equipped with monocular vision systems.

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Section: Trifocal Tensor Estimation Using Convex Optimisationmentioning

confidence: 99%

“…A new cooperative motion estimation scheme using a modified Particle Swarm Optimisation (PSO) algorithm is presented. 8 A cooperative motion estimation is addressed for a networked multi-camera system. 9 A decentralised cooperative trajectory estimation for autonomous underwater vehicles is presented as well.…”

Section: Introductionmentioning

confidence: 99%

Robust L_∞ convex optimisation for UAVs cooperative motion estimation

Boulekchour

Aouf

Richardson

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Britto et al [19] applied a combination of a PSO and AMEA to reduce the computational complexity and search points. A cooperative motion estimation algorithm based on multi-warm PSO was proposed by Jalloul et al [20]. In this method, information exchange about the motion vectors was found effective in exploiting spatial correlation, refining the motion search and, therefore, leads to a faster convergence and demonstrated improvement in the resulting motion vectors.…”

Section: Introductionmentioning

confidence: 99%

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Bhattacharjee

Kumar

Pandey

et al. 2018

Computers & Electrical Engineering

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“…Exploitation of temporal redundancy between successive frames in a video codec is possible Final manuscript received on May 30, 2017. 1,2 The authors are with DDepartment of Electrical Engineering National Institute of Technology Rourkela, India, E-mail: ypatnaiknitrkl@gmail.com and dpatra@nitrkl.ac.in with a motion compensation technique by predicting the current frame from the past one. Motion estimation (ME) has a vital role in inter-frame prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Motion estimation (ME) has a vital role in inter-frame prediction. From the available motion estimation techniques, the block-matching algorithm (BMA) is considered the most popular because of its simplicity and has been adopted in most of the video coding standards, namely H.264/AVC and H.265/HEVC [2]. In this case, BMA frames are broken down to blocks, and individual motion vectors are calculated for each block.…”

Section: Introductionmentioning

confidence: 99%

Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization

Patnaik

Patra

2017

ECTI-CIT

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Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%

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A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

Cited by 10 publications

References 37 publications

Robust L_∞ convex optimisation for UAVs cooperative motion estimation

Robust L_∞ convex optimisation for UAVs cooperative motion estimation

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization

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A novel Cooperative Motion Estimation Algorithm based on Particle Swarm Optimization and its multicore implementation

Cited by 10 publications

References 37 publications

Robust L∞ convex optimisation for UAVs cooperative motion estimation

Robust L∞ convex optimisation for UAVs cooperative motion estimation

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization

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Robust L_∞ convex optimisation for UAVs cooperative motion estimation

Robust L_∞ convex optimisation for UAVs cooperative motion estimation