An efficient hybrid framework for visual tracking using Exponential Quantum Particle Filter and Mean Shift optimization

Dash, Prajna Parimita; Patra, Dipti

doi:10.1007/s11042-020-08999-z

Cited by 4 publications

(1 citation statement)

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“…A new approach for speech recognition based on the specific coding of time and frequency characteristics of speech using CNNs has been presented [27]. Visual object tracking by using an exponential quantum particle filter and mean shift optimization has been presented as an another challenge for object tracking [28].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

Kamsing

Torteeka

Boonpook

et al. 2020

Applied Computational Intelligence and Soft Computing

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To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.

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Section: Introductionmentioning

confidence: 99%