Autonomous Movement of Wheelchair by Cameras and YOLOv7

Sarker, Md Abdul Baset; Sola-Thomas, Ernesto; Jamieson, Collin; Imtiaz, Masudul H.

doi:10.3390/asec2022-13834

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…An alternative recent approach [ 20 ], on the contrary, is based on the use of a single camera, pointed in the direction of travel of the PW, with the goal of classifying obstacles in its path. However, this methodology does not make distance measurements and, therefore, does not allow the application of an accurate path control logic.…”

Section: Introductionmentioning

confidence: 99%

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Gallo,

Shallari,

Carratù

et al. 2024

Sensors

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The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver’s feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.

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Section: Introductionmentioning

confidence: 99%

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Gallo,

Shallari,

Carratù

et al. 2024

Sensors

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“…In facial recognition systems and other biometric security systems, camera-based solutions are widely used for different modalities [15,[19][20][21][22][23][24][25], and low-powered singleboard computers are popular for image analysis-based projects because of their smaller size, low power consumption, etc. [26][27][28][29][30]. In our research, we provided an open-source hardware and software solution that can run on a low-power single-board computer with a commercially available camera in a fully customizable system.…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Face-Aware Capture System

Sarker,

Hossain,

Venkataswamy

et al. 2024

Electronics

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Poor-quality facial images pose challenges in biometric authentication, especially in passport photo acquisition and recognition. This study proposes a novel and open-source solution to address these issues by introducing a real-time facial image quality analysis utilizing computer vision technology on a low-power single-board computer. We present an open-source complete hardware solution that consists of a Jetson processor, a 16 MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. To ensure the integrity and confidentiality of captured facial data, Advanced Encryption Standard (AES) is used for secure image storage. Using the pilot data collection, the system demonstrated its ability to capture high-quality images, achieving 98.98% accuracy in storing images of acceptable quality. This open-source, readily deployable, secure system offers promising potential for diverse real-time applications such as passport verification, security systems, etc.

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“…Camera-based solutions are widely used for various modalities in biometric systems [7][8][9]. For image analysis-based projects, single-board computers such as Jetson Nano [3,[9][10][11], Google Coral [12,13], and Raspberry Pi [14] are popular for their smaller size and low power consumption. Our study used a Jetson Nano processor with a commercially available camera.…”

Section: Introductionmentioning

confidence: 99%

AI Face-Capture for First-time Passport Application

SARKER,

Imtiaz

2024

Preprint

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This paper presents the development and implementation of a Pan-Tilt-Zoom (PTZ) camera interfaced with a Jetson processor hosting Artificial Intelligence (AI) algorithms, designed to address the challenges of traditional biometric capture for first-time passport photos of young children. The system enhances the accuracy, efficiency, and adaptability of facial biometric capture by recognizing and accommodating the dynamic and diverse behaviors of children. Utilizing AI, the system detects faces and analyzes facial features using 68 landmark points, ensuring high-quality images that meet ISO standards. These technical specifications, implementation process, and performance evaluations of the developed system are provided in this paper, demonstrating the potential of the proposed AI Face-Capture to advance biometric identification and verification for young children.

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Autonomous Movement of Wheelchair by Cameras and YOLOv7

Cited by 4 publications

References 19 publications

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

An Open-Source Face-Aware Capture System

AI Face-Capture for First-time Passport Application

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