16,209 research outputs found

    Detecting and tracking multiple interacting objects without class-specific models

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    We propose a framework for detecting and tracking multiple interacting objects from a single, static, uncalibrated camera. The number of objects is variable and unknown, and object-class-specific models are not available. We use background subtraction results as measurements for object detection and tracking. Given these constraints, the main challenge is to associate pixel measurements with (possibly interacting) object targets. We first track clusters of pixels, and note when they merge or split. We then build an inference graph, representing relations between the tracked clusters. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked clusters as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups of objects during interactions. Experimental results on multiple video sequences are shown

    A Comparative Perspective on Italy’s Human Capital Accumulation

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    This paper reviews the evolution of educational institutions and outcomes over the 150 years since Italy’s unification, and discusses their interaction with national and regional growth patterns. While initial educational conditions contributed to differentiate across regions the early industrial take off in the late 19th century, and formal education does not appear to have played a major role in the postwar economic boom, the slowdown of Italy’s economy since the 1990s may be partly due to interactions between its traditionally low human capital intensity and new comparative advantage patterns, and to the deterioration since the 1970s of the educational system’s organization.Education systems, tracking, economic growth, regional convergence

    PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

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    Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

    A study in the cognition of individuals’ identity: Solving the problem of singular cognition in object and agent tracking

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    This article compares the ability to track individuals lacking mental states with the ability to track intentional agents. It explains why reference to individuals raises the problem of explaining how cognitive agents track unique individuals and in what sense reference is based on procedures of perceptual-motor and epistemic tracking. We suggest applying the notion of singular-files from theories in perception and semantics to the problem of tracking intentional agents. In order to elucidate the nature of agent-files, three views of the relation between object- and agent-tracking are distinguished: the Independence, Deflationary and Organism-Dependence Views. The correct view is argued to be the latter, which states that perceptual and epistemic tracking of a unique human organism requires tracking both its spatio-temporal object-properties and its agent-properties

    Activity Recognition Using Probabilistic Timed Automata

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    A Rasch model and rating system for continuous responses collected in large-scale learning systems

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    An extension to a rating system for tracking the evolution of parameters over time using continuous variables is introduced. The proposed rating system assumes a distribution for the continuous responses, which is agnostic to the origin of the continuous scores and thus can be used for applications as varied as continuous scores obtained from language testing to scores derived from accuracy and response time from elementary arithmetic learning systems. Large-scale, high-stakes, online, anywhere anytime learning and testing inherently comes with a number of unique problems that require new psychometric solutions. These include (1) the cold start problem, (2) problem of change, and (3) the problem of personalization and adaptation. We outline how our proposed method addresses each of these problems. Three simulations are carried out to demonstrate the utility of the proposed rating system

    How Mobile Devices are Transforming Disaster Relief and Public Safety

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    With its growing usage, mobile technology is greatly improving disaster relief and public safety efforts. Countries around the world face threats from natural disasters, climate change, civil unrest, terrorist attacks, and criminal activities, among others. Mobile devices, tablets, and smart phones enable emergency providers and the general public to manage these challenges and mitigate public safety concerns.In this paper, part of the Brookings Mobile Economy Project, we focus on how mobile technology provides an early warning system, aids in emergency coordination, and improves public communications. In particular, we review how mobile devices assist with public safety, disaster planning, and crisis response. We explain how these devices are instrumental in the design and functioning of integrated, multi-layered communications networks. We demonstrate how they have helped save lives and ameliorate human suffering throughout the world

    Disabling Racial Repetition

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