scholarly journals A Vision-Based System for Monitoring Elderly People at Home

2020 ◽  
Vol 10 (1) ◽  
pp. 374 ◽  
Author(s):  
Marco Buzzelli ◽  
Alessio Albé ◽  
Gianluigi Ciocca
Keyword(s):  
Computer Vision ◽  
Elderly People ◽  
Assisted Living ◽  
Scientific Community ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Training Set ◽  
Single Dataset ◽  
Care Of Elderly ◽  
Different Sources

Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.

scholarly journals A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
L. Payá ◽  
A. Gil ◽  
O. Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Scientific Community ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Omnidirectional Vision ◽  
Vision Sensors ◽  
Source Of Information

Nowadays, the field of mobile robotics is experiencing a quick evolution, and a variety of autonomous vehicles is available to solve different tasks. The advances in computer vision have led to a substantial increase in the use of cameras as the main sensors in mobile robots. They can be used as the only source of information or in combination with other sensors such as odometry or laser. Among vision systems, omnidirectional sensors stand out due to the richness of the information they provide the robot with, and an increasing number of works about them have been published over the last few years, leading to a wide variety of frameworks. In this review, some of the most important works are analysed. One of the key problems the scientific community is addressing currently is the improvement of the autonomy of mobile robots. To this end, building robust models of the environment and solving the localization and navigation problems are three important abilities that any mobile robot must have. Taking it into account, the review concentrates on these problems; how researchers have addressed them by means of omnidirectional vision; the main frameworks they have proposed; and how they have evolved in recent years.

scholarly journals More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Qiaoling Han ◽  
Jianbo Su ◽  
Yue Zhao
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Facial Feature ◽  
Sparse Learning ◽  
Learning Method ◽  
Training Set ◽  
Feature Description ◽  
The Face

In the actual face recognition applications, the sample sets are updated constantly. However, most of the face recognition models with learning strategy do not consider this fact and using a fixed training set to learn the face recognition models for once. Besides that, the testing samples are discarded after the testing process is completed. Namely, the training and testing processes are separated and the later does not give a feedback to the former for better recognition results. To attenuate these problems, this paper proposed an online sparse learning method for face recognition. It can update the salience evaluation vector in real time to construct a dynamical facial feature description model. Also, a strategy for updating the gallery set is proposed in this proposed method. Both the dynamical facial feature description model and the gallery set are employed to recognize faces. Experimental results show that the proposed method improves the face recognition accuracy, comparing with the classical learning models and other state-of-the-art face recognition methods.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

2021 ◽  
Vol 11 (5) ◽  
pp. 603
Author(s):  
Chunlei Shi ◽  
Xianwei Xin ◽  
Jiacai Zhang
Keyword(s):  
Machine Learning ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Target Domain ◽  
Sample Distribution ◽  
Machine Learning Methods ◽  
First Time

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

scholarly journals Virtual Geosite Communication through a WebGIS Platform: A Case Study from Santorini Island (Greece)

2021 ◽  
Vol 11 (12) ◽  
pp. 5466
Author(s):  
Federico Pasquaré Mariotto ◽  
Varvara Antoniou ◽  
Kyriaki Drymoni ◽  
Fabio Luca Bonali ◽  
Paraskevi Nomikou ◽  
...  
Keyword(s):  
Bronze Age ◽  
Scientific Community ◽  
Earth Science ◽  
State Of The Art ◽  
Immersive Virtual Reality ◽  
Geological Processes ◽  
3D Exploration ◽  
Effusive Eruptions ◽  
Lay Public

We document and show a state-of-the-art methodology that could allow geoheritage sites (geosites) to become accessible to scientific and non-scientific audiences through immersive and non-immersive virtual reality applications. This is achieved through a dedicated WebGIS platform, particularly handy in communicating geoscience during the COVID-19 era. For this application, we selected nine volcanic outcrops in Santorini, Greece. The latter are mainly associated with several geological processes (e.g., dyking, explosive, and effusive eruptions). In particular, they have been associated with the famous Late Bronze Age (LBA) eruption, which made them ideal for geoheritage popularization objectives since they combine scientific and educational purposes with geotourism applications. Initially, we transformed these stunning volcanological outcrops into geospatial models—the so called virtual outcrops (VOs) here defined as virtual geosites (VGs)—through UAV-based photogrammetry and 3D modeling. In the next step, we uploaded them on an online platform that is fully accessible for Earth science teaching and communication. The nine VGs are currently accessible on a PC, a smartphone, or a tablet. Each one includes a detailed description and plenty of annotations available for the viewers during 3D exploration. We hope this work will be regarded as a forward model application for Earth sciences' popularization and make geoheritage open to the scientific community and the lay public.

System for Assistance on Bath of Bedridden Elderly People

2014 ◽  
Author(s):  
Karolina Bezerra ◽  
José Machado ◽  
Vitor Carvalho ◽  
Filomena Soares ◽  
Bruno Silva ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Elderly People ◽  
Conceptual Design ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
The Other ◽  
Mechatronic System ◽  
Rehabilitation Centers ◽  
Aged Person

From Ambient Assisted Living (AAL) perspective it is important to have information regarding the type of care needed by bedridden elderly people (BEP) living in their homes, in order to support independence, autonomy and maximize their quality of life. Some basic tasks as eating, taking a bath and the hygiene cares may be difficult to execute, regarding that almost always the main caregiver is the other element of the couple (husband or wife). Following this trend, the development of mechatronic devices is of upmost importance in creating solutions to facilitate these tasks. This paper presents the conceptual design of a mechatronic system especially devoted to the assistance during the bath of BEP. Issues as reducing the number of caregivers to only one to assist the bath and reducing the system’s handling complexity (because most of the time it will be used by an aged person) are considered. Visits to rehabilitation centers and hospitals, and respective working meetings, are considered in the development of the proposed mechatronic system.

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