SAFEST: A Framework for Early Security Triggers in Public Spaces

International audiencePublic spaces such as airports, railway stations or stadiums bring together large numbers of people on a quite limited space to use a security-sensitive infrastructure. Electronic security systems may help to provide better and faster security, as well as safety for the general public. Application scenarios may include intrusion detection and monitoring of large crowds in order to provide guidance in case of unexpected events (e.g., a mass panic). However, current security systems used within the public infrastructure are typically expensive, non-trivial to deploy, difficult to operate and maintain, prone to malfunction due to individual component failures, and generally lack citizen privacy-friendliness. The advent of novel, large-scale distributed security systems based on wireless, lightweight sensors may enhance security and safety in public spaces. In this realm, SAFEST is a project aiming at analyzing the social context of area surveillance and developing a system that can fulfill this task, both in terms of technology as well as acceptance by the general public. The targeted system will operate in a distributed way, collect anonymized data, securely transfer this data to a central location for evaluation, and - if necessary - notify the operator or issue alerts directly to the general public. Work on the technical aspects of the system is accompanied by social studies investigating the individual perception of risk and the methods for reaching public acceptance of the technical solutions

F-formation Detection: Individuating Free-standing Conversational Groups in Images

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.This work was supported in part by the Spanish Government through the projects INRISCO under Grant TEC2014-54335-C4-1-R, Grant TEC2014-54335-C4-2-R, Grant TEC2014-54335-C4-3-R, and Grant TEC2014-54335-C4-4-R, in part by the MAGOS under Grant TEC2017-84197-C4-1-R, Grant TEC2017-84197-C4-2-R, and Grant TEC2017-84197-C4-3-R, in part by the European Regional Development Fund (ERDF), and in part by the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)

Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach

We develop a network of Bayesian agents that collectively model the mental states of teammates from the observed communication. Using a generative computational approach to cognition, we make two contributions. First, we show that our agent could generate interventions that improve the collective intelligence of a human-AI team beyond what humans alone would achieve. Second, we develop a real-time measure of human's theory of mind ability and test theories about human cognition. We use data collected from an online experiment in which 145 individuals in 29 human-only teams of five communicate through a chat-based system to solve a cognitive task. We find that humans (a) struggle to fully integrate information from teammates into their decisions, especially when communication load is high, and (b) have cognitive biases which lead them to underweight certain useful, but ambiguous, information. Our theory of mind ability measure predicts both individual- and team-level performance. Observing teams' first 25% of messages explains about 8% of the variation in final team performance, a 170% improvement compared to the current state of the art.Comment: 9 pages, Accepted at AAAI 202

Collaborative Solutions to Visual Sensor Networks

Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications in large surveillance areas. Although potentially powerful, VSNs also present unique challenges that could hinder their practical deployment because of the unique camera features including the extremely higher data rate, the directional sensing characteristics, and the existence of visual occlusions. In this dissertation, we first present a collaborative approach for target localization in VSNs. Traditionally; the problem is solved by localizing targets at the intersections of the back-projected 2D cones of each target. However, the existence of visual occlusions among targets would generate many false alarms. Instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in 2D cones and generate the so-called certainty map of targets non-existence. We also propose distributed integration of local certainty maps by following a dynamic itinerary where the entire map is progressively clarified. The accuracy of target localization is affected by the existence of faulty nodes in VSNs. Therefore, we present the design of a fault-tolerant localization algorithm that would not only accurately localize targets but also detect the faults in camera orientations, tolerate these errors and further correct them before they cascade. Based on the locations of detected targets in the fault-tolerated final certainty map, we construct a generative image model that estimates the camera orientations, detect inaccuracies and correct them. In order to ensure the required visual coverage to accurately localize targets or tolerate the faulty nodes, we need to calculate the coverage before deploying sensors. Therefore, we derive the closed-form solution for the coverage estimation based on the certainty-based detection model that takes directional sensing of cameras and existence of visual occlusions into account. The effectiveness of the proposed collaborative and fault-tolerant target localization algorithms in localization accuracy as well as fault detection and correction performance has been validated through the results obtained from both simulation and real experiments. In addition, conducted simulation shows extreme consistency with results from theoretical closed-form solution for visual coverage estimation, especially when considering the boundary effect