Real-Time Object Detection for Visually Challenged People

Vaidya, Sunit; Shah, Naisha; Shah, Niti; Shankarmani, Radha

doi:10.1109/iciccs48265.2020.9121085

Cited by 43 publications

(11 citation statements)

References 5 publications

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“…This structure gives a precision of 73. 6 RGB profundity cameras are used by Tian to recognize steps including walker crosslines [31], [32]. Hough Transformation calculation was used for setting up the pictures and afterward gathering is finished utilizing the SVM and achieved an accuracy of 93.90…”

Section: Related Workmentioning

confidence: 99%

Smart Assistive System for Visually Impaired People Obstruction Avoidance Through Object Detection and Classification

et al. 2022

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Recent progress in innovation is making the life prosper, simpler and easier for common individual. The World Health Organization (WHO) statistics indicate that a large amount of people experience visual losses, because of which they encounter many difficulties in everyday jobs. Hence, our goal is to structure a modest, secure, wearable, and versatile framework for visually impaired to help them in their daily routines. The proposed methodology utilizes Raspberry-Pi 4B, Camera, Ultrasonic Sensor and Arduino, mounted on the stick of the individual. We take pictures of the scene and afterwards pre-process these pictures with the help of Viola Jones and TensorFlow Object Detection algorithm. The said techniques are used to detect objects. We also used an ultrasonic sensor mounted on a servomotor to measure the distance between the blind person and obstacles. The presented research utilizes simple calculations for its execution, and detects the obstructions with a notably high efficiency. When contrasted with different frameworks, this framework is a minimal effort, convenient, and simple to wear.

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Section: Related Workmentioning

confidence: 99%

Smart Assistive System for Visually Impaired People Obstruction Avoidance Through Object Detection and Classification

et al. 2022

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“…Bhole et al [16], used deep learning techniques such as Single Shot Detector (SSD) and Inception V3 to classify bank concurrency notes in real-time environment. Vaidya et al [17] presented an image processing method with machine learning approach to classify the multiclass objects.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Audio Assistive System for Visually Impaired People

Masud¹,

Jeribi²,

Alhameed³

et al. 2022

Computers, Materials &Amp; Continua

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Vision impairment is a latent problem that affects numerous people across the globe. Technological advancements, particularly the rise of computer processing abilities like Deep Learning (DL) models and emergence of wearables pave a way for assisting visually-impaired persons. The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment. But, in real-time scenarios, these systems are inconsistent in providing effective guidance for visually-impaired people. In addition to object detection, extra information about the location of objects in the scene is essential for visually-impaired people. Keeping this in mind, the current research work presents an Efficient Object Detection Model with Audio Assistive System (EODM-AAS) using DL-based YOLO v3 model for visually-impaired people. The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people. The presented model involves a DL-based YOLO v3 model for multi-label object detection. Besides, the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people. In order to validate the detection performance of the presented method, a detailed simulation analysis was conducted on four datasets. The simulation results established that the presented model produces effectual outcome over existing methods.

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“…In the fourth stage, the information obtained in the count is analyzed while the last stage refers to the visualization of the detections. Likewise, Vaidya et al [18] compare various configurations of neural networks and select a series of algorithms for the detection of people and compare them with the YOLO algorithm. They are able to process up to 45 frames per second, and for the YOLOv3 version, they demonstrate a high requirement of hardware tools, so they carry out the study with the Tiny YOLO version.…”

Section: Literature Reviewmentioning

confidence: 99%

Background subtraction and yolo algorithm

Rondón¹,

Casadiego²,

Delgado³

2021

ing. Solidar

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Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.

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Real-Time Object Detection for Visually Challenged People

Cited by 43 publications

References 5 publications

Smart Assistive System for Visually Impaired People Obstruction Avoidance Through Object Detection and Classification

Smart Assistive System for Visually Impaired People Obstruction Avoidance Through Object Detection and Classification

Deep Learning Based Audio Assistive System for Visually Impaired People

Background subtraction and yolo algorithm

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