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Carla Maria Modena

Permanent researcher
  • Phone: 0461314508
  • FBK Povo
Short bio

Carla Maria Modena received her degree in Mathematics from the University of Trento in 1985. In 1986, she worked as research assistant (Wissenschaftlicher Mitarbeiter) in Ökonometrie at the Institut für Volkswirtschaftslehre, Universität Regensburg, Germany.
She joined ITC-irst, now FBK (Trento - Italy) in 1987, where she works with the Technologies of Vision (TeV) research unit.

She is interested in text detection and extraction for document image understanding (DIU and OCR) and for scene understanding (TIS). She works mainly on computer vision with applicative tasks (for example, traffic scene analyis, people re-identification). To prevent the risk of reinventing the wheel, she pays a close attention to the state-of-the art before starting each task in which she is involved in, using her ability to fully exploit the net resources and to analyse trends in upcoming conferences.

She is a member of IAPR-GIRPR.

Research interests
text localization in scene people re-identification
  1. C. M. Modena; S. Messelodi,
    Recognition and tracking of embedded text in sport videos: a case study,
    Abstract - We present an athlete recognition module designed for broadcast videos, forming part of a system designed for the personalization of sport video broadcast. The aim of this module is the identification of athletes in the scene through the reading of names or numbers printed on their uniforms and to identify frames where athletes are visible. Using an adaptation of a previously published algorithm we extract text from individual frames and then read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to an a-priori list of athletes' names (or numbers), to provide a presence score for each athlete. Text regions are subsequently tracked in following frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, can also be exploited to provide a denser labelling of the video sequences.,
  2. Stefano Messelodi; Carla Maria Modena; Michele Zanin; Francesco De Natale; Fabrizio Granelli; Enrico Betterle; Andrea Guarise,
    vol. 36,
    n. 3 Part 1,
    , pp. 4213 -
  3. Stefano Messelodi; Carla Maria Modena; Gianni Cattoni,
    vol. 28,
    n. 13,
    , pp. 1719 -
  4. Stefano Messelodi; Carla Maria Modena; Michele Zanin; Fabrizio Granelli; Francesco De Natale; Enrico Betterle; Andrea Guarise,
    Extended Traffic Data Collection by Intelligent Vehicles,
    The elaboration of floating car data, i.e. data collected by vehicles moving on road network, is relevant for traffic management and for private service providers, which can bundle updated traffic information with navigation services. Floating data, in its extended acceptation, contains not only time and location provided by an on-board positioning system, but also information coming from othervarious vehicle sensors.In this report we describe our extended data collection system, in which vehicles are able to collect data about their local environment, namely the presence of roadworks and traffic slowdowns, by analyzing visual data taken by a looking forward camera and data from the on-board Electronic Control Unit. Upon detection of such events, apacket is set up containing time, position, vehicle data, results of on-board elaboration, one or more images of the road ahead and an estimation of the local traffic level. Otherwise, the transmitted packet containing only the minimal data, making its size adaptive to the environment surrounding the vehicle.,
  5. S. Messelodi; C. M. Modena; M. Zanin,
    vol. 8,
    n. 1-2,
    , pp. 17 -
  6. S. Messelodi; C. M. Modena,
    vol. 7,
    n. b,
    , pp. 51 -
  7. S. Messelodi; C. M. Modena; N. Segata; M. Zanin,
    Image Analysis and Processing – ICIAP 2005,
    , pp. 163-
    , (13th International Conference on Image Analysis and Processing - ICIAP 2005,
    Cagliari, Italy,
    09/06/2005 - 09/08/2005)
  8. S. Messelodi; C.M. Modena,
    A Computer Vision System for Traffic Accident Risk Measurement: A Case Study,
    Abstract - A reliable estimation of the safety level of the roads is a valuable tool for detecting critical points in the road infrastructure, planning and implement countermeasures, and evaluating their impact on the traffic. A method for the computation of the accident risk is proposed, which is based on microscopic traffic data collected automatically by a video-based monitoring system, i.e. class, speed, and tra jectory of each single road-user. The benefit of the proposed method is twofold: the risk level is computed without statistics on past accidents, and its computation is fully automated, i.e. it does not require a manual collection of traffic data. The paper presents the definition of the proposed risk index and
    describes its application to a real case: the evaluation of the accident risk at an urban intersection, before and after the reorganization of its geometry. The proposed risk index, although based only on those parameters that are automatically measurable, seems to reflect the expectation of traffic experts in evaluating the impact of intervention to improve the safety level of the intersection.
  9. C. Corridori; D. Giordani; P. Lombardi; S. Messelodi; C. M. Modena; M. Zanin,
    An in-vehicle vision system for dangerous situation detection,
    Morlacchi Editore,
    , (Conferenza Italiana sui Sistemi Intelligenti 2004 – 2° Convegno del Gruppo Italiano Ricercatori in Pattern Recognition GIRPR-04,
    Perugia, Italy,
    from 09/15/2004 to 09/15/2004)
  10. S. Messelodi; C. M. Modena; M. Zanin,
    A computer vision system for the detection and classification of vehicles at urban road intersections,
    This paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences from pole-mounted video cameras at urban crossroads.
    The system is flexible with respect to road geometry and camera position, permitting data collection from several monitored crosses. It uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing background subtraction and a feature-based tracking methodology. For each detected vehicle, the system is able to describe its path, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one.
    Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a one hundred thousand people town in northern Italy