Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

Marketing AI recruitment: The next phase in job application and selection

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

http://jultika.oulu.fi/files/nbnfi-fe2018102338631.pdf

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

Radar and RGB-Depth Sensors for Fall Detection: A Review

Vision-based monitoring for assisted living is gaining increasing attention, especially in multi-modal monitoring systems owing to the several advantages of vision-based sensors. In this paper, a detailed survey of some of the important vision-based patient monitoring applications is presented, namely (a) fall detection (b) action and activity monitoring (c) sleep monitoring (d) respiration and apnea monitoring (e) epilepsy monitoring (f) vital signs monitoring and (g) facial expression monitoring. The challenges and state-of-art technologies in each of these applications is presented. This is the first work to present such a comprehensive survey with the focus on a set of seven most common applications pertaining to patient monitoring. Potential future directions are presented while also considering practical large scale deployment of vision-based systems in patient monitoring. One of the important conclusions drawn is that rather than applying generic algorithms, use of the application context of patient monitoring can be a useful way to develop novel techniques that are robust and yet cost-effective.

Vision-based patient monitoring: a comprehensive review of algorithms and technologies

Falls are one of the major risks of injury for elderly living alone at home. Computer vision-based systems offer a new, low-cost and promising solution for fall detection. This paper presents a new fall-detection tool, based on a commercial RGB-D camera. The proposed system is capable of accurately detecting several types of falls, performing a real time algorithm in order to determine whether a fall has occurred. The proposed approach is based on evaluating the contraction and the expansion speed of the width, height and depth of the 3D human bounding box, as well as its position in the space. Our solution requires no pre-knowledge of the scene (i.e. the recognition of the floor in the virtual environment) with the only constraint about the knowledge of the RGB-D camera position in the room. Moreover, the proposed approach is able to avoid false positive as: sitting, lying down, retrieve something from the floor. Experimental results qualitatively and quantitatively show the quality of the proposed approach in terms of both robustness and background and speed independence.

/pdf/fall-detection-in-indoor-environment-with-kinect-sensor-4i1senfemq.pdf

Fall detection in indoor environment with kinect sensor

Recognizing facial emotions is an important aspect of interpersonal communication that may be impaired in various neurological disorders: Asperger's syndrome, Autism, Schizoid Personality, Parkinsonism, Urbach-Wiethe, Amyotrophic Lateral Sclerosis, Bipolar Disorder, Depression, Alzheimer's desease. Although it is not possible to define unique emotions, we can say that are mental states, physiological and psychophysiological changes associated with internal or external stimuli, both natural and learned. This paper highlights certain requirements that the specification approach would need to meet if the production of such tools were to be achievable. In particular, we present an innovative and still experimental tool to support diagnosis of neurological disorders by means of facial-expressions-monitoring. At the same time, we propose a new study to measure several impairments of patients recognizing emotions ability, and to improve the reliability of using them in computer aided diagnosis strategies.

A new tool to support diagnosis of neurological disorders by means of facial expressions

Introduction and objective: the purpose of this work is to design and implement an innovative tool to recognize 16 different human gestural actions and use them to predict 7 different emotional states. The solution proposed in this paper is based on RGB and depth information of 2D/3D images acquired from a commercial RGB-D sensor called Kinect. Materials: the dataset is a collection of several human actions made by different actors. Each action is performed by each actor for three times in each video. 20 actors perform 16 different actions, both seated and upright, totalling 40 videos per actor. Methods: human gestural actions are recognized by means feature extractions as angles and distances related to joints of human skeleton from RGB and depth images. Emotions are selected according to the state-of-the-art. Experimental results: despite truly similar actions, the overall-accuracy reached is approximately 80%. Conclusions and future works: the proposed work seems to be back-ground- and speed-independent, and it will be used in the future as part of a multimodal emotion recognition software based on facial expressions and speech analysis as well.

A new tool for gestural action recognition to support decisions in emotional framework

In this paper we present the results of an experimental Italian research project finalized to support the classification process of the two behavioural status (resonance and dissonance) of a candidate applying for a job position. The proposed framework is based on an innovative system designed and implemented to extract and process the non-verbal expressions like facial, gestural and prosodic of the subject, acquired during the whole job interview session. In principle, we created our own database, containing multimedia data extracted, by different software modules, from video, audio and 3D sensor streams and then used SVM classifiers that perform in terms of accuracy 72%, 79% and 63% respectively for facial, vocal and gestural features. ANN classifiers have also been used, obtaining comparable results. Finally, we combined all the three domains and then reported the results of this last classification test proving that the experimental proposed work seems to perform in a very encouraging way.

Dario D'Ambruoso

Papers

Fall detection in indoor environment with kinect sensor

A new tool to support diagnosis of neurological disorders by means of facial expressions

A new tool for gestural action recognition to support decisions in emotional framework

Evaluation of Resonance in Staff Selection through Multimedia Contents