Generic and Robust Method for Head Pose Estimation

Hajraoui, Abdellatif; Sabri, Mohamed

doi:10.11591/ijeecs.v4.i2.pp439-446

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…As shown in ( 6) is used to calculate the Euclidian distance between two points. D=√∑ (𝑋 𝑖 − 𝑌 𝑖 ) 2 𝑛 𝑖=1 (6) Support vector machine (SVM) used for classification and regression problems [36]. The goal is to find a hyperplane in an N-dimensional space and separately classify the query data point.…”

Section: Description Of the Classifiersmentioning

confidence: 99%

A machine learning approach for driver identification

Khan

Ali

Haque

et al. 2023

IJEECS

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Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.

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Section: Description Of the Classifiersmentioning

confidence: 99%

A machine learning approach for driver identification

Khan

Ali

Haque

et al. 2023

IJEECS

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“…Features which can be used for tracking head pose are nose,mouth, and face position [18], [14]. There is also approach to use texture as position descriptor [19], [20]. However, tracking requires mandatory initialization of head position.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker

et al. 2018

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Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker's bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure.

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Generic and Robust Method for Head Pose Estimation

Cited by 2 publications

References 16 publications

A machine learning approach for driver identification

A machine learning approach for driver identification

Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker

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