A Comparative Study on Gesture Recognition Systems for Car Infotainment Systems

Mahetalia, Jahnavi; Mehta, Raj Mattias; Varudandi, Shaunak; Samdani, Krishna

doi:10.1109/icscan.2018.8541236

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Additionally, in the automotive industry, gesture recognition is being integrated into vehicular systems, enabling drivers to control features such as navigation, entertainment, and climate without diverting attention from driving, thus augmenting safety and convenience [25].…”

Section: Cmentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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Section: Cmentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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“…Recently, smart infotainment systems based on biometrics have been introduced for driver convenience [1,2]. Smart vehicles are actively researching in-vehicle-infotainment (IVI) or audio-video navigation (AVN) device technologies that can provide driver-centered lifelogging and 3D user interface (UI) services [3,4].…”

Section: Introductionmentioning

confidence: 99%

Identification System Based on Resolution Adjusted 2D Spectrogram of Driver’s ECG for Intelligent Vehicle

Choi

Lim

Pan

2022

Mobile Information Systems

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Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.

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A multistudy investigation of drivers and passengers’ gesture and voice input preferences for in-vehicle interactions

Bilius

Vatavu

2020

Journal of Intelligent Transportation Systems

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A Comparative Study on Gesture Recognition Systems for Car Infotainment Systems

Cited by 6 publications

References 3 publications

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Identification System Based on Resolution Adjusted 2D Spectrogram of Driver’s ECG for Intelligent Vehicle

A multistudy investigation of drivers and passengers’ gesture and voice input preferences for in-vehicle interactions

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