Search Results - (( model evaluation poe algorithm ) OR ( motion estimation swarm algorithm ))*

Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

“…Block Matching Algorithm (BMA) is a technique used to minimize the computational complexity of motion estimation in video coding application. …”
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MARKERLESS ARTICULATED HUMAN MOTION TRACKING USING HIERARCHICAL MULTI-SWARM COOPERATIVE PARTICLE SWARM OPTIMIZATION by SAINI, SANJA Y

“…Most recently. the swarm-intelligence based PSO algorithm have been gaining momentum in this tield.…”
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A review on particle swarm optimization algorithm and its variants to human motion tracking by Saini, S., Rambli, D.R.B.A., Zakaria, M.N.B., Sulaiman, S.B.

“…The goal of human motion capture is to estimate the joints angles of human body at any time. …”
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SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS by YAZID, EDWAR

“…The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
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Non-probabilistic approach to cooperative position tracking in large swarm of simple mobile robots using triangular cross-observation by Din, Abdul Sattar

“…Unfortunately, many of the most popular approaches for cooperative localization in literature today are probabilistic, which are computationally complex and less tolerant to any deviation from their predetermined probabilistic motion and observation models. This research focuses on devising a computationally simpler non-probabilistic cooperative position tracking algorithm specifically for a large swarm of simple mobile robots with the purpose of reducing the error accumulation in the position estimates of an individual robot due to noise in odometric measurement. …”
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