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Stefano Messelodi

Head of Unit
  • Phone: 0461314509
  • FBK Povo
Short bio

Stefano Messelodi was born in Arco (Italy) in 1961. He graduated in computer science from the University of Milan (Italy).  Since 1986 he is working in FBK (ITC-irst), Trento, Italy, where he coordinated the Technologies of Vision research unit till 2012. His research interests include text localization in scene, semantic image labelling and dynamic scene understanding. He served as a reviewer for several journals and conferences. He is a member of IEEE Society and International Association for Pattern Recognition.
He currently coordinates the research unit TeV.

Research interests
people re-identification text localization in scene document image analysis traffic analysis
  1. P. Lombardi; M. Zanin; S. Messelodi,
    Unified Stereovision for Ground, Road, and Obstacle Detection,
    , pp. 783-
    , (IEEE Intelligent Vehicles Symposium - IV 2005,
    Las Vegas, NV, USA,
    06/06/2005 - 06/08/2005)
  2. P. Lombardi; M. Zanin; S. Messelodi,
    Switching Models for Vision-Based On-Board Road Detection,
    , pp. 67-
    , (IEEE Conference on Intelligent Transportation Systems,
    Vienna, Austria,
    09/13/2005 - 09/16/2005)
  3. 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)
  4. C. Andreatta; M. Lecca; S. Messelodi,
    Memory-based Object Recognition in digital Images,
    AKA Akademische Verlagsgesellschaft,
    , pp. 33-
    , (19th International Fall Workshop- Vision Modeling and Visualization,
    Erlangen, Germany,
    11/16/2005 - 11/18/2005)
  5. 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.
  6. 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)
  7. C. Andreatta; M. Lecca; S. Messelodi,
    RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
    , (RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
  8. Qi. Xu; R. Brunelli; S. Messelodi; J. Zhang; M. Li,
    Image Coherence Based Adaptive Sampling for Image Synthesis,
    Computational Science and its Applications - ICCSA 2004,
    , pp. 693-
    , (International Conference on Computational Science and its Applications - ICCSA 2004,
    Assisi, Italy,
  9. Q. Xu; L. Ma; M. Li; W. Wang; J. Cai; R. Brunelli; S. Messelodi,
    Fuzzy weighted average filtering for mixture noises,
    , pp. 18-
    , (Third International Conference on Image and Graphics (ICIG'04),
    Hong Kong, China,
    12/18/2004 - 12/20/2004)
  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