Algorithm of local features fusion and modified covariance-matrix technique for hand motion position estimation and hand gesture trajectory tracking approach

Thabet, Eman; Khalid, Fatimah; Sulaiman, Puteri Suhaiza; Yaakob, Razali

doi:10.1007/s11042-020-09903-5

Cited by 5 publications

(3 citation statements)

References 72 publications

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“…Simple tracking method, limited accuracy 2020Smith [14] Dense skin surface tracking algorithm based on visual and physical models Skin surface High computational load, long tracking delays 2021Thabet [15] Local key corner points and Gabor-Canny Hog feature tracking for feature points…”

Section: Feature Pointsmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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With the emergence of deep learning methodologies, vision-based gesture recognition technology has continuously advanced. This paper primarily delves into four main stages of vision-based gesture recognition: gesture segmentation, gesture tracking, feature extraction, and gesture classification. It sequentially introduces pertinent techniques from representative literature spanning from 2018 to 2023. Based on this analysis, the current status of vision-based gesture recognition technology is examined, paving the way for predicting its future trends and developments.

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Section: Feature Pointsmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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“…Recently, based on the excellent properties of autocorrelation signal, Chen et al. [20, 21] proposed a fusion algorithm, which performs autocorrelation and averaging on multi‐segment signals, and then using expanded autocorrelation (EA) or modified covariance (MC) algorithm [22, 23] for frequency estimation. Compared with other algorithms in time domain, the accuracy is improved, and the method is easy to implement, but the disadvantage is that the accuracy drops significantly at low SNR.…”

Section: Introductionmentioning

confidence: 99%

“…Then Chen et al [19] carried out coherent averaging on multisegment signals in the time domain to improve the SNR and used FFT for frequency estimation, but the method needs to ensure that each segment of the signal has the same initial phase, which is difficult to implement in practice. Recently, based on the excellent properties of autocorrelation signal, Chen et al [20,21] proposed a fusion algorithm, which performs autocorrelation and averaging on multi-segment signals, and then using expanded autocorrelation (EA) or modified covariance (MC) algorithm [22,23] for frequency estimation. Compared with other algorithms in time domain, the accuracy is improved, and the method is easy to implement, but the disadvantage is that the accuracy drops significantly at low SNR.…”

Section: Introductionmentioning

confidence: 99%

A high‐precision frequency estimation algorithm based on multi‐segment signal fusion

Liu¹,

Liu²,

He³

et al. 2022

IET Radar Sonar & Navi

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As the current multi-segment signal fusion algorithms cannot meet the requirement of high-precision frequency estimation at different signal-to-noise ratios (SNRs), an adaptive fusion algorithm is proposed in this paper. At high SNR, an autocorrelation weighted fusion algorithm is utilised, which adopts the real-time weighted fusion of multi-segment autocorrelation signals, resulting in denoised sinusoidal signals. Then the frequency is obtained by minimising the error function based on coarse estimation. At low SNR, an improved spectrum fusion algorithm is adopted, which uses the three-parameter sinefitting method to determine the phase difference compensation factor, and the multisegment signal spectrum is weighted to obtain a long signal spectrum with continuous phase. As a result, the frequency can be estimated by spectral peak search. Simulation results show that the proposed algorithm, compared with existing algorithms, not only improves the estimation accuracy by about 20%-50% but also can perform highprecision frequency estimation for multi-segment signals of different lengths, frequencies, and abnormal situations.

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Computer Vision Human Activity Recognition Using Cumulative Difference Energy Representation-Based Features and Employing Machine Learning Techniques

Sathya¹,

Gokulakannan

2022

Computational Methods and Data Engineering

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Algorithm of local features fusion and modified covariance-matrix technique for hand motion position estimation and hand gesture trajectory tracking approach

Cited by 5 publications

References 72 publications

Review of vision-based gesture recognition technology

Review of vision-based gesture recognition technology

A high‐precision frequency estimation algorithm based on multi‐segment signal fusion

Computer Vision Human Activity Recognition Using Cumulative Difference Energy Representation-Based Features and Employing Machine Learning Techniques

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