TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) 2019
DOI: 10.1109/tencon.2019.8929719
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Gaussian Process Auto Regression for vehicle center coordinates Trajectory Prediction

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Cited by 8 publications
(4 citation statements)
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“…We used the polyfit to estimate the pedestrian’s actual depth value in order to reduce the noise of the pedestrian’s depth value. There are many other methods available for predicting trajectory, speed estimation, and prediction, especially in the automobile industry, such as in [ 49 ]. In our paper, the gradient of the polyfit was used to estimate the velocity of the pedestrian.…”
Section: Resultsmentioning
confidence: 99%
“…We used the polyfit to estimate the pedestrian’s actual depth value in order to reduce the noise of the pedestrian’s depth value. There are many other methods available for predicting trajectory, speed estimation, and prediction, especially in the automobile industry, such as in [ 49 ]. In our paper, the gradient of the polyfit was used to estimate the velocity of the pedestrian.…”
Section: Resultsmentioning
confidence: 99%
“…In the real world, almost all data can satisfy the Gaussian distribution [32]. The Gaussian mixture model can calculate the joint probability distribution of the moving object trajectory, which is mainly used to predict coordinates [33,34]. The Gaussian mixture model has high accuracy in short-term prediction, but it is prone to be affected by the complexity of the data, which results in reduced accuracy and low practicability.…”
Section: Probabilistic Statistical Modelmentioning
confidence: 99%
“…There are many papers that use machine learning and deep neural network algorithms which are used as a reference for this paper's trajectory prediction. They are articles by Lim et al (2019), Goli et al (2018), Heravi and Khanmohammadi (2011), Park et al (2018).…”
Section: Limitationmentioning
confidence: 99%