Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles

Pan, Janice; Appia, Vikram; Villarreal, Jesse; Weaver, Lucas; Kwon, Do-Kyoung

doi:10.1109/cvprw.2017.157

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…The other problem is defined as image stitching in [12]. Several studies, including (i) pairing the corresponding detected key points and the (ii) interpolation of image elements captured by vehicle mirror cameras and stereo pairs of cameras at the rear of the vehicle, were performed in order to solve the stitching problem in [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Smart Driver Behavior Recognition and 360-Degree Surround-View Camera for Electric Buses

Cuma,

Dükünlü,

Yirik

2023

Electronics

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The automotive industry’s focus on driver-oriented issues underscores the critical importance of driver safety. This paper presents the development of advanced driver assistance system (ADAS) algorithms specifically tailored for an electric bus (e-bus) to enhance safety. The proposed approach incorporates two key components: a 360-degree surround-view system and driver behavior recognition utilizing the You Only Look Once V5 (YOLO_V5) method. The adoption of YOLO_V5 in ADASs enables rapid response by processing multiple class probabilities and region proposals within an image instantaneously. Additionally, ADAS implementation includes an image processing-based surround-view system utilizing OpenCV. In order to evaluate the performance of the proposed algorithms regarding a smart e-bus, comprehensive experimental studies were conducted. The driver behavior recognition system underwent rigorous testing using various images captured by an onboard camera. Similarly, the surround-view system’s performance was verified in diverse driving scenarios, including regular driving, parking, and parking in near-to-line situations. The results demonstrate the viability and effectiveness of the proposed system, validating its potential to significantly improve driver safety in electric buses. This paper provides a comprehensive overview of the work accomplished by emphasizing the specific contributions of the 360-degree surround-view system, driver behavior recognition using YOLO_V5, and the experimental validation conducted for an e-bus.

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Section: Introductionmentioning

confidence: 99%

Smart Driver Behavior Recognition and 360-Degree Surround-View Camera for Electric Buses

Cuma,

Dükünlü,

Yirik

2023

Electronics

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“…A Surround View Monitoring System, one of the vision-based ADAS, uses camera parameters to generate a bird's eye view image with a FOV that contains all of the information around the vehicle [1,2]. A panoramic rear-view system also uses camera parameters to stitch side-view image and rear-view image to generate a panoramic image [3,4]. These vision-based ADAS transforms the captured image using camera parameters to generate the desired FOV image.…”

Section: Introductionmentioning

confidence: 99%

A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System

Lee

2020

Sensors

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We propose an automatic camera calibration method for a side-rear-view monitoring system in natural driving environments. The proposed method assumes that the camera is always located near the surface of the vehicle so that it always shoots a part of the vehicle. This method utilizes photographed vehicle information because the captured vehicle always appears stationary in the image, regardless of the surrounding environment. The proposed algorithm detects the vehicle from the image and computes the similarity score between the detected vehicle and the previously stored vehicle model. Conventional online calibration methods use additional equipment or operate only in specific driving environments. On the contrary, the proposed method is advantageous because it can automatically calibrate camera-based monitoring systems in any driving environment without using additional equipment. The calibration range of the automatic calibration method was verified through simulations and evaluated both quantitatively and qualitatively through actual driving experiments.

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Reflecting health: smart mirrors for personalized medicine

et al. 2018

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Inexpensive embedded computing and the related Internet of Things technologies enable the recent development of smart products that can respond to human needs and improve everyday tasks in an attempt to make traditional environments more “intelligent”. Several projects have augmented mirrors for a range of smarter applications in automobiles and homes. The opportunity to apply smart mirror technology to healthcare to predict and to monitor aspects of health and disease is a natural but mostly underdeveloped idea. We envision that smart mirrors comprising a combination of intelligent hardware and software could identify subtle, yet clinically relevant changes in physique and appearance. Similarly, a smart mirror could record and evaluate body position and motion to identify posture and movement issues, as well as offer feedback for corrective actions. Successful development and implementation of smart mirrors for healthcare applications will require overcoming new challenges in engineering, machine learning, computer vision, and biomedical research. This paper examines the potential uses of smart mirrors in healthcare and explores how this technology might benefit users in various medical environments. We also provide a brief description of the state-of-the-art, including a functional prototype concept developed by our group, and highlight the directions to make this device more mainstream in health-related applications.

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Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles

Cited by 6 publications

References 28 publications

Smart Driver Behavior Recognition and 360-Degree Surround-View Camera for Electric Buses

Smart Driver Behavior Recognition and 360-Degree Surround-View Camera for Electric Buses

A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System

Reflecting health: smart mirrors for personalized medicine

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