Detection of immovable objects on visually impaired people walking aids

One consequence of a visually impaired (blind) person is a lack of ability in the activities related to the orientation and mobility. Blind person uses a stick as a tool to know the objects that surround him/her.The objective of this research is to develop a tool for blind person which is able to recognize what object in front of him/her when he/she is walking. An attached camera will obtain an image of an object which is then processed using template matching method to identify and trace the image of the object. After getting the image of the object, furthermore calculate and compare it with the data training. The output is produced in the form of sound that in accordance with the object. The result of this research is that the best slope and distance for the template matching method to properly detect silent objects is 90 degrees and 2 meters

RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing

Stairs are common building structures in urban environments, and stair detection is an important part of environment perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a neural network architecture with RGB and depth map inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes. In addition, we design a line clustering algorithm for the postprocessing of detection results, which can make full use of the detection results to obtain the geometric stair parameters. Experiments on our dataset show that our method can achieve better accuracy and recall compared with existing state-of-the-art deep learning methods, which are 5.64% and 7.97%, respectively, and our method also has extremely fast detection speed. A lightweight version can achieve 300 + frames per second with the same resolution, which can meet the needs of most real-time detection scenes

Modelo de sistema de locomoción autónomo: una ayuda para las personas con discapacidad visual en la prevención de la Covid-19

Covid-19 generó una pandemia que afectó la vida de millones de personas en todo el mundo, sus sistemas de salud y economía, provocando la muerte de miles de personas. Según la Organización Mundial de la Salud, miles de millones de personas en el mundo tienen algún tipo de discapacidad visual y en Brasil, según el Instituto Brasileño de Geografía y Estadística, este número corresponde a millones de personas. En situación de Covid-19, las personas con discapacidad visual son altamente vulnerables al riesgo de contagio debido a la necesidad de tocar superficies y dependen de la ayuda y la proximidad de terceros. Proponer un modelo de sistema de locomoción autónomo en ambientes exteriores e interiores para personas con discapacidad visual. El modelo propuesto se basa en dispositivos capaces de aportar mejoras la calidad de vida y movilidad de las personas con discapacidad visual, permitiéndoles moverse de forma independiente en entornos exteriores e interiores, evitando así el contacto con superficies y terceros. Dividido en cinco pasos: (i) análisis de trabajos similares; (ii) modelo de detección de obstáculos y movimientos para asegurar la distancia; (iii) modelo utilizando Google Maps para la locomoción en entornos externos; (iv) modelo con la elaboración de mapas del entorno para la locomoción en interiores; (v) funcionalidades y ontología del sistema propuesto. El sistema propuesto puede componer un conjunto de medidas de protección contra el Covid-19 establecido por el Instituto Federal Fluminense, que puede ser utilizado por otras instituciones y gobiernos en apoyo de la Política Nacional de Movilidad

Assessing neural network scene classification from degraded images

Scene recognition is an essential component of both machine and biological vision. Recent advances in computer vision using deep convolutional neural networks (CNNs) have demonstrated impressive sophistication in scene recognition, through training on large datasets of labeled scene images (Zhou et al. 2018, 2014). One criticism of CNN-based approaches is that performance may not generalize well beyond the training image set (Torralba and Efros 2011), and may be hampered by minor image modifications, which in some cases are barely perceptible to the human eye (Goodfellow et al. 2015; Szegedy et al. 2013). While these “adversarial examples” may be unlikely in natural contexts, during many real-world visual tasks scene information can be degraded or limited due to defocus blur, camera motion, sensor noise, or occluding objects. Here, we quantify the impact of several image degradations (some common, and some more exotic) on indoor/outdoor scene classification using CNNs. For comparison, we use human observers as a benchmark, and also evaluate performance against classifiers using limited, manually selected descriptors. While the CNNs outperformed the other classifiers and rivaled human accuracy for intact images, our results show that their classification accuracy is more affected by image degradations than human observers. On a practical level, however, accuracy of the CNNs remained well above chance for a wide range of image manipulations that disrupted both local and global image statistics. We also examine the level of image-by-image agreement with human observers, and find that the CNNs' agreement with observers varied as a function of the nature of image manipulation. In many cases, this agreement was not substantially different from the level one would expect to observe for two independent classifiers. Together, these results suggest that CNN-based scene classification techniques are relatively robust to several image degradations. However, the pattern of classifications obtained for ambiguous images does not appear to closely reflect the strategies employed by human observers

Система керування літаючим роботом-пилососом

Магістерська дисертація освітньо-кваліфікаційного рівня “магістр” на тему «Система керування літаючим роботом-пилососом». 116 сторінок, 33 рисунки, 27 таблиць, 9 додатків, 20 джерел. Мета і задачі дослідження. Метою даної роботи є покращення універсальності робота-пилососа за рахунок збільшення можливих зон прибирання через додавання функції короткочасного польоту. Об’єкт дослідження. Система керування роботом з використанням елементів штучного інтелекту. Предмет дослідження. Методи аналізу та інтелектуальної обробки даних для забезпечення польоту.Master's dissertation of educational qualification level "master" on the topic "Control system for a flying robot vacuum cleaner". 116 pp., 33 figs., 27 tab., 9 appendices, 20 sources. The purpose and objectives of the study. The purpose of this work is to improve the versatility of the robot vacuum cleaner by increasing the possible cleaning areas by adding the function of short-term flight. Object of study. Robot control system using elements of artificial intelligence. Subject of study. Methods of analysis and intelligent data processing to ensure flight

Ability of head-mounted display technology to improve mobility in people with low vision: a systematic review

Purpose: The purpose of this study was to undertake a systematic literature review on how vision enhancements, implemented using head-mounted displays (HMDs), can improve mobility, orientation, and associated aspects of visual function in people with low vision. Methods: The databases Medline, Chinl, Scopus, and Web of Science were searched for potentially relevant studies. Publications from all years until November 2018 were identified based on predefined inclusion and exclusion criteria. The data were tabulated and synthesized to produce a systematic review. Results: The search identified 28 relevant papers describing the performance of vision enhancement techniques on mobility and associated visual tasks. Simplifying visual scenes improved obstacle detection and object recognition but decreased walking speed. Minification techniques increased the size of the visual field by 3 to 5 times and improved visual search performance. However, the impact of minification on mobility has not been studied extensively. Clinical trials with commercially available devices recorded poor results relative to conventional aids. Conclusions: The effects of current vision enhancements using HMDs are mixed. They appear to reduce mobility efficiency but improved obstacle detection and object recognition. The review highlights the lack of controlled studies with robust study designs. To support the evidence base, well-designed trials with larger sample sizes that represent different types of impairments and real-life scenarios are required. Future work should focus on identifying the needs of people with different types of vision impairment and providing targeted enhancements. Translational Relevance: This literature review examines the evidence regarding the ability of HMD technology to improve mobility in people with sight loss

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling