CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People

Abstract

Visually impaired persons (VIPs) comprise a significant portion of the population, and they are present around the globe and in every part of the world. In recent times, technology proved its presence in every domain, and innovative devices assist humans in their daily lives. In this work, a smart and intelligent system is designed for VIPs to assist mobility and ensure their safety. The proposed system provides navigation in real-time using an automated voice. Though VIPs wouldn't be able to see objects in their surroundings, they can sense and visualize the roaming environment. Moreover, a web-based application is developed to ensure their safety. The user of this application can turn the on-demand function for sharing his/her location with the family while compromising privacy. Through this application, the family members of VIPs would be able to track their movement (get location and snapshots) while being at their homes. Hence, the device allows VIPs to visualize the environment and ensure their security. Such a comprehensive device was a missing link in the existing literature. The application uses MobileNet architecture due to its low computational complexity to run on low-power end devices. To assess the efficacy of the proposed system, six pilot studies have been performed that reflected satisfactory results. For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. Moreover, a score-based quantitative comparative analysis is performed using the supported features of devices. It is found that the proposed system has outperformed the existing devices having a total score of 9.1/10, which is 8% higher than the second-best.

IBGS: A Wearable Smart System to Assist Visually Challenged

“…However, binocular cameras are particularly sensitive to ambient lighting and are not suitable for scenes that lack texture [8]. While meeting the common needs of blind people, many studies have also explored other services to provide more detailed guarantees for blind people to go out [9]- [12]. The tracking system proposed by F. Ashiq et al provide navigation in real-time using an automated voice.…”

“…This work develops a web-based application to help guardians track the user's outdoor activities, including obtaining location and snapshots. The snapshots are acquired using the deep Convolution Neural Network (CNN) model, and the dataset contains more than 1,000 categories [9]. N. Kumar et al proposed a wearable mask and a stick, which can achieve path edge detection applying the YOLOv3 algorithm, whereas the dataset contains only 25 categories.…”

Distance Assessment by Object Detection—For Visually Impaired Assistive Mechatronic System

“…There are two major trends in the development of object recognition methods, depending on the technology used: systems based on the function for visualization of the environment (cameras/video), and systems without visualization of the environment (IR sensors, laser, ultrasonic, Bluetooth, or GPS) [8]. Such systems are included in practical applications in many fields, but two are particularly distinct: applications in the field of freight and passenger transport for self-driving cars, and in the medical field for the assistance of people with visual impairments.…”

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

“…Computer vision and digital image processing are used in various applied domains such as remote sensing, pose detection, decision making, path detection, defect detection, and automatic driving [21][22][23][24][25][26]. e recent focus of research in this field is the use of deep learning models that have shown good results in various applied domains [27][28][29].…”