Eye Tracking Algorithm Based on Multi Model Kalman Filter

Bagherzadeh, S. H. Ziafati; Toosizadeh, Saeed

doi:10.28991/hij-2022-03-01-02

Cited by 15 publications

(8 citation statements)

References 16 publications

(17 reference statements)

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“…Thus, the process from the stage of production to the stage of consumption can be made shorter [14]. In the agricultural industry, it is possible to increase efficiency and productivity by using this type of agricultural technology [15,16]. 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000…”

Section: Figure 1 Top Countries In Dates Production (Fao 2019) [10]mentioning

confidence: 99%

External Features-Based Approach to Date Grading and Analysis with Image Processing

et al. 2022

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The analysis and classification of dates is based on their external features: size, appearance, shape, and colour. The process is currently performed manually after harvesting as part of the post-harvesting process. Grading manually is tedious because it usually results in time delays, product quality risks, and it is associated with time and cost delays as well. Although the use of computers and information technology has seen tremendous growth in many small and large sectors, it has been in its infancy in the cultivation of fruit and dates. Using image processing algorithms, we can enhance human vision capabilities through analysis and make images easier to comprehend. A major objective of computer vision-based algorithms for classifying and sorting of dates is to make the procedure fully automated by minimizing the manual component involved in the process. This paper presents an image processing-based algorithm that uses machine learning techniques to extract the characteristics of colour intensity and colour homogeneity, allowing us to grade images in a more timely and automated manner. In order to obtain the results, we extracted the appearance of the date images based on an image processing algorithm. It is used as a validation element for the results that the quality of dates-fruit images can be evaluated through the prior selection process in both separate and in groups. This study has managed to achieve a rate of 95% accuracy in data classification. Doi: 10.28991/ESJ-2022-06-04-03 Full Text: PDF

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Section: Figure 1 Top Countries In Dates Production (Fao 2019) [10]mentioning

confidence: 99%

External Features-Based Approach to Date Grading and Analysis with Image Processing

et al. 2022

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“…Bagherzadeh andToosizadeh [12] Eye tracking systems are important components of Human Machine Interface (HMI) equipment, and this paper presents an algorithm for increasing eye tracking efficiency using multi-model Kalman filters.Multi-model Kalman filters employ multiple models for object estimation, aiming to boost efficiency and reduce estimation errors. Based on the typical behavior of the human eye, the algorithm first recognizes the initial eye position using the Support Vector Machine (SVM), and then uses a multi-model Kalman filter to predict the eye's position in the next frame using constant speed and acceleration models.…”

Section: Literature Reviewmentioning

confidence: 99%

AdaBoost based Random forest model for Emotion classification of Facial images

Gubbala,

Kumar,

Sowjanya

2023

MethodsX

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“…The performance of an aided navigation system is directly affected by optimal state estimation techniques used in the integration or sensor fusion scheme. Kalman Filter (KF) is the most widely used optimal state estimator in many theorical and industrial applications including integrated navigation systems (6)(7)(8)(9)(10)(11). To achieve an optimal solution in the KF, determining proper models for the system and stochastic noises is a key factor problem (12).…”

Section: Introductionmentioning

confidence: 99%

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Ansari,

Jamali,

Fatehi-Dindarlou

et al. 2024

IJE

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Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter.

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Eye Tracking Algorithm Based on Multi Model Kalman Filter

Cited by 15 publications

References 16 publications

External Features-Based Approach to Date Grading and Analysis with Image Processing

External Features-Based Approach to Date Grading and Analysis with Image Processing

AdaBoost based Random forest model for Emotion classification of Facial images

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

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