scholarly journals Machine Learning Based Computer Vision Application for Visually Disabled People

2021 ◽  
pp. 488-494
Author(s):  
Shubhada Mone ◽  
Nihar Salunke ◽  
Omkar Jadhav ◽  
Arjun Barge ◽  
Nikhil Magar
Keyword(s):  
Computer Vision ◽  
Distance Estimation ◽  
Depth Map ◽  
Depth Estimation ◽  
Indoor Navigation ◽  
Disabled People ◽  
Automatic Driving ◽  
Object Distance ◽  
Physical Tools ◽  
Visually Disabled

With the easy availability of technology, smartphones are playing an important role in every person’s life. Also, with the advancements in computer vision based research, Automatic Driving cars, Object Recognition, Depth Map Prediction, Object Distance Estimation, have reached commendable levels of intelligence and accuracy. Combining the research and technological advancements, we can be hopeful in creating a computer vision based mobile-application which will help guide visually disabled people in performing their day to day tasks with easily available mobile applications. With our study, the visually disabled can perform simple tasks like outdoor/indoor navigation without encountering obstacles, also they can avoid accidental collisions with objects in their surroundings. Currently, there are very few applications which provide the same assistance to the visually impaired. Using physical tools like sticks is a very common practice when it comes to avoiding obstacles in a visually disabled person’s path. Our study will be focused on object detection and depth estimation techniques- two of the most popular and advanced fields in Intelligent Computer vision studies. We have explored more on the traditional challenges and future hopes of incorporating these techniques on embedded devices.

Vision based distance estimation from single RGB camera using field of view and magnification measurements –an AI based non triangulation technique for person distance estimation in surveillance areas

2020 ◽  
pp. 1-17
Author(s):  
P.J.A. Alphonse ◽  
K.V. Sriharsha
Keyword(s):  
Learning Algorithm ◽  
Focal Length ◽  
Distance Estimation ◽  
Ground Truth ◽  
Depth Estimation ◽  
Physical Distance ◽  
Field Of View ◽  
Gradient Boosting ◽  
Object Distance ◽  
Binocular Stereo

Depth data from conventional cameras in monitoring fields provides a thorough assessment of human behavior. In this context, the depth of each viewpoint must be calculated using binocular stereo, which requires two cameras to retrieve 3D data. In networked surveillance environments, this drives excess energy and also provides extra infrastructure. We launched a new computational photographic technique for depth estimation using a single camera based on the ideas of perspective projection and lens magnification property. The person to camera distance (or depth) is obtained from understanding the focal length, field of view and magnification characteristics. Prior to finding distance, initially real height is estimated using Human body anthropometrics. These metrics are given as inputs to the Gradient-Boosting machine learning algorithm for estimating Real Height. And then magnification and Field of View measurements are extracted for each sample. The depth (or distance) is predicted on the basis of the geometrical relationship between field of view, magnification and camera at object distance. Using physical distance and height measurements taken in real time as ground truth, experimental validation is performed and it is inferred that with in 3m–7 m range, both in indoor and outdoor environments, the camera to person distance (Preddist) anticipated from field of view and magnification is 91% correlated with actual depth at a confidence point of 95% with RMSE of 0.579.

scholarly journals Relative Importance of Binocular Disparity and Motion Parallax for Depth Estimation: A Computer Vision Approach

2019 ◽  
Vol 11 (17) ◽  
pp. 1990 ◽  
Author(s):  
Mostafa Mansour ◽  
Pavel Davidson ◽  
Oleg Stepanov ◽  
Robert Piché
Keyword(s):  
Computer Vision ◽  
Stereo Vision ◽  
Binocular Disparity ◽  
Motion Parallax ◽  
Depth Map ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Camera Motion ◽  
Angular Velocities ◽  
Motion Parameters

Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two cameras as well as the camera motion parameters serve as the input to our motion parallax-based depth estimation algorithm. The measured camera motion parameters include translational and angular velocities. Reference distance to the tracked features is provided by a LiDAR. Overall, our results show that at short distances stereo vision is more accurate, but at large distances the combination of parallax and camera motion provide better depth estimation. Therefore, by combining the two cues, one obtains depth estimation with greater range than is possible using either cue individually.

CamNav: a computer-vision indoor navigation system

2021 ◽  
Author(s):  
Abdel Ghani Karkar ◽  
Somaya Al-Maadeed ◽  
Jayakanth Kunhoth ◽  
Ahmed Bouridane
Keyword(s):  
Computer Vision ◽  
Navigation System ◽  
Indoor Navigation ◽  
Indoor Navigation System

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

scholarly journals Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xin Yang ◽  
Qingling Chang ◽  
Xinglin Liu ◽  
Siyuan He ◽  
Yan Cui
Keyword(s):  
Depth Map ◽  
Depth Estimation ◽  
Multi Scale ◽  
Monocular Depth

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

New Electro-Optic and Display Technology for Visually Disabled People

2013 ◽  
pp. 687-718
Author(s):  
Ricardo Vergaz Benito ◽  
César Vega-Colado ◽  
María Begoña Coco ◽  
Rubén Cuadrado ◽  
Juan Carlos Torres-Zafra ◽  
...  
Keyword(s):  
Visual Impairment ◽  
New Technologies ◽  
Age Related Macular Degeneration ◽  
Disabled People ◽  
Age Related ◽  
Technical Aids ◽  
The World ◽  
World Information ◽  
Materials Used ◽  
Visually Disabled

The aim of the chapter is to review the most recent advances in electro-optical technologies applied to visually disabled people. The World Health Organization (WHO) estimates that the number of people in the world with some kind of visual impairment is 285 million, with 246 million of these persons in a partially sighted or Low Vision (LV) condition. The top three causes of visual impairment are uncorrected refractive errors, cataracts and glaucoma, followed by age-related macular degeneration. On the other hand, Head Mounted Displays or electro-optical materials used in liquid crystal or electrochromic devices can be used in technical aids for LV. In this chapter, the authors review how disabled people receive real world information using these new technologies, how the recently developed electro-optical technical aids can improve visual perception, and how these LV aids do work, from a technological point of view.

Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

2011 ◽  
pp. 209-228
Author(s):  
Muhammad Tariq Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Genetic Programming ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Depth Map ◽  
Depth Estimation ◽  
Shape Reconstruction ◽  
Optical Methods ◽  
3D Shape ◽  
Focus Measure ◽  
Single Focus

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.

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