Can adversarial networks hallucinate occluded people with a plausible aspect?

When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world

Context Change Detection for an Ultra-Low Power Low-Resolution Ego-Vision Imager

With the increasing popularity of wearable cameras, such as GoPro or Narrative Clip, research on continuous activity monitoring from egocentric cameras has received a lot of attention. Research in hardware and software is devoted to find new efficient, stable and long-time running solutions; however, devices are too power-hungry for truly always-on operation, and are aggressively duty-cycled to achieve acceptable lifetimes. In this paper we present a wearable system for context change detection based on an egocentric camera with ultra-low power consumption that can collect data 24/7. Although the resolution of the captured images is low, experimental results in real scenarios demonstrate how our approach, based on Siamese Neural Networks, can achieve visual context awareness. In particular, we compare our solution with hand-crafted features and with state of art technique and propose a novel and challenging dataset composed of roughly 30000 low-resolution images

Osservatorio comorbidità nei grandi anziani con Fibrillazione Atriale

La Fibrillazione Atriale (FA) è una patologia correlata all’età - colpisce il 16% degli ultra ottantacinquenni - che aumenta di circa cinque volte il rischio di ictus cerebrale. La terapia anticoagulante ha un ruolo centrale nel trattamento della FA, e la sua applicazione nel paziente anziano è ostacolata dalla presenza di comorbidità, di politerapia e dalla necessità di gestione delle possibili interazioni farmacologiche. Ulteriori elementi di difficoltà derivano dalla interazione tra diversi specialisti, dall’inerzia prescrittiva, dalla complessità del sistema di accesso alle cure e, non ultimo, anche dalle difficoltà di gestione del paziente anziano in terapia anticoagulante da parte dei caregiver familiari. Obiettivo dell’Osservatorio è stato identificare le problematiche dei pazienti con FA riguardo la gestione della terapia anticoagulante in presenza di diverse patologie e terapie concomitanti, attraverso il contributo del Board multistakeholder, dell’analisi della comunicazione on line sulla FA, nonché a due survey su medici e pazienti. È stato delineato un quadro della condizione dei pazienti anziani con FA e delle difficoltà nella gestione quotidiana della malattia, a partire dal quale sono state formulate alcune proposte di intervento rivolte ai decisori, ai clinici e in generale a tutti coloro che sono chiamati alla gestione concreta della malattia insieme a pazienti e caregive

Frequency of left ventricular hypertrophy in non-valvular atrial fibrillation

Left ventricular hypertrophy (LVH) is significantly related to adverse clinical outcomes in patients at high risk of cardiovascular events. In patients with atrial fibrillation (AF), data on LVH, that is, prevalence and determinants, are inconsistent mainly because of different definitions and heterogeneity of study populations. We determined echocardiographic-based LVH prevalence and clinical factors independently associated with its development in a prospective cohort of patients with non-valvular (NV) AF. From the "Atrial Fibrillation Registry for Ankle-brachial Index Prevalence Assessment: Collaborative Italian Study" (ARAPACIS) population, 1,184 patients with NVAF (mean age 72 \ub1 11 years; 56% men) with complete data to define LVH were selected. ARAPACIS is a multicenter, observational, prospective, longitudinal on-going study designed to estimate prevalence of peripheral artery disease in patients with NVAF. We found a high prevalence of LVH (52%) in patients with NVAF. Compared to those without LVH, patients with AF with LVH were older and had a higher prevalence of hypertension, diabetes, and previous myocardial infarction (MI). A higher prevalence of ankle-brachial index 640.90 was seen in patients with LVH (22 vs 17%, p = 0.0392). Patients with LVH were at significantly higher thromboembolic risk, with CHA2DS2-VASc 652 seen in 93% of LVH and in 73% of patients without LVH (p <0.05). Women with LVH had a higher prevalence of concentric hypertrophy than men (46% vs 29%, p = 0.0003). Logistic regression analysis demonstrated that female gender (odds ratio [OR] 2.80, p <0.0001), age (OR 1.03 per year, p <0.001), hypertension (OR 2.30, p <0.001), diabetes (OR 1.62, p = 0.004), and previous MI (OR 1.96, p = 0.001) were independently associated with LVH. In conclusion, patients with NVAF have a high prevalence of LVH, which is related to female gender, older age, hypertension, and previous MI. These patients are at high thromboembolic risk and deserve a holistic approach to cardiovascular prevention

Recognizing social relationships from an egocentric vision perspective

In this chapter we address the problem of partitioning social gatherings into interacting groups in egocentric scenarios. People in the scene are tracked, and their head pose and 3D location are estimated. Following the formalism of the f-formation, we define as regards the orientation and distance inherently social pairwise features capable of describing how two people stand in relation to one another. We present a structural SVM-based approach to learn how to weight each component of the feature vector depending on the social situation being applied to. To better understand the social dynamics, we also estimate what we call the social relevance of each subject in a group using a saliency attentive model. Extensive tests on two publicly available datasets show that our solution achieves encouraging results when detecting social groups and their relevant subjects in challenging egocentric scenarios

An IoT-based Smart Museum for a new interactive cultural experience

Art and culture have always played an important role in human beings lives. Over the centuries, hundred of museums and art galleries have preserved our diverse cultural heritage and served as important sources of education and learning. Museums are nowadays a tool of entertainment such as theatres or cinemas. Today, museums and art galleries usually provide visitors either with paper booklets or with audio guides. Visits at museums are often considered boring, because it is hard for museums curators to catch the attention of tourists. In particular, it is difficult to define in advance a tour for all the visitors, because interests may vary from person to person. Interests are different from children to adults, students group from single visitor, casual visitor to fond-visitor. Interactive and personalized museum tours need to be developed. In this perspective, a significant contribution can be given by the next Internet of Things (IoT), which involves the extension of the Internet to small and low-cost “things” that are thought to realize smart environments in order to provide new services to the users. IoT aims to create a better world for people, where smart objects around us know what we like, what we want and act accordingly without explicit gestures [1]. Taking into account all these considerations, we have developed a system able to address all the above- described issues. This work is partially supported by the research project “PON04a2_D - DICET LivingLab Di Cultura e Tecnologia – INMOTO – OR.C.HE.S.T.R.A.”, funded by the Italian Ministry of Education, University and Research (MIUR). More in detail, the solution developed enables wearable devices, interacting with an IoT- based smart environment, to act as museum guides, providing a real interactive cultural experience

A Location-Aware Architecture for an IoT-Based Smart Museum

The Internet of Things, whose main goal is to automatically predict users’ desires, can find very inter- esting opportunities in the art and culture field, as the tourism is one of the main driving engines of the modern society. Currently, the innovation process in this field is growing at a slower pace, so the cultural heritage is a prerogative of a restricted category of users. To address this issue, a significant technological improvement is necessary in the culture-dedicated locations, which do not usually allow the installation of hardware infrastructures. In this paper, we design and validate a no-invasive indoor location-aware architecture able to enhance the user experience in a museum. The system relies on the user’s smartphone and a wearable device (with image recognition and localization capabilities) to au- tomatically deliver personalized cultural contents related to the observed artworks. The proposal was validated in the MUST museum in Lecce (Italy)