A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals

Luo, Zhidan; Guo, Wei; Liu, Qingfu; Tse, Yiuman

doi:10.1002/for.2935

Cited by 1 publication

(2 citation statements)

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“…The algorithm then uses this model to predict the object’s place in the next frame and update the object’s position accordingly. This approach is efficient and robust to small changes in the object’s motion ( Luo et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

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Postures anomaly tracking and prediction learning model over crowd data analytics

Aljuaid

Akhter

Alsufyani

et al. 2023

PeerJ Computer Science

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Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.

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Section: Methodsmentioning

confidence: 99%

“…On the other hand, if the object’s motion is known to be non-smooth, the compressive tracking algorithm may be more appropriate. Combining both algorithms achieves more accurate and optimized results ( Luo et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%