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Oswald Lanz

Permanent researcher
  • Phone: 0461314511
  • FBK Povo
Short bio

I received the MSc in Mathematics ('00) and a Ph.D in Computer Science ('05) from the University of Trento, and the Habilitation to Associate Professor (in Italy, ASN16 09/H1).

I am a Researcher at the Fondazione Bruno Kessler, Trento, Italy since 2005, tenured since 2008. My research has been mainly in the field of people tracking, audio-visual tracking, pose estimation and group detection with application to monitoring and behavior analysis. From 2012 to end of 2015 I have been Head of Computer Vision Research Unit Technologies of Vision at the Fondazione Bruno Kessler. In 2016 I have been launching an innovation project in a home automation SME. 

My main research interests are currently in deep learning with structured output prediction applied to video classification and tracking. I hold a number of international patents on video tracking.

Publications
  1. O. Lanz,
    An Information Theoretic Rule for Sample Size Adaptation in Particle Filtering,
    2007
    , pp. 317-
    322
    , (International Conference on Image Analysis and Processing - ICIAP,
    Modena, Italy,
    from 09/10/2007 to 09/14/2007)
  2. O. Lanz; R. Brunelli,
    Multimodal Technologies for Perception,
    Heidelberg,
    Springer,
    vol.4625/2008,
    2007
    , pp. 287-
    296
    , (Classification of Events, Activities and Relatinships, Evaluation and Workshop - CLEAR,
    Baltimore, MD, USA,
    from 5/8/2007 to 5/9/2007)
  3. O. Lanz,
    Method and apparatus for tracking a number of objects or object parts in image sequences.
    2007,
    A method for tracking a number of objects or object parts in image sequences utilizes a Bayesian-like approach to object tracking, computing, at each time a new image is available, a probability distribution over all possible target configurations for that time. The Bayesian-like approach to object tracking computes a probability distribution for the previous image, at time (t−1), is propagated to the new image at time (t) according to a probabilistic model of target dynamics, obtaining a predicted distribution at time (t). The Bayesian-like approach to object tracking also aligns the predicted distribution at time (t) with the evidence contained in the new image at time (t) according to a probabilistic model of visual likelihood.
  4. O. Lanz,
    Approximate Bayesian Multibody Tracking,
    in «IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE»,
    vol. 28,
    n. 9,
    2006
    , pp. 1436 -
    1449
  5. Roberto Brunelli; Alessio Brutti; Paul Chippendale; Oswald Lanz; Maurizio Omologo; Piergiorgio Svaizer; Francesco Tobia,
    A Generative Approach to Audio-Visual Person Tracking,
    Multimodal Technologies for Perception,
    Springer,
    vol.4122/2007,
    2006
    , (First International Evaluation Workshop on Classification of Events, Activities and Relationships - CLEAR'06,
    Southampton, UK,
    from 04/10/2006 to 04/12/2006)
  6. O. Lanz; F. Tobia; R. Brunelli,
    Multi-view Appearance Model for Visual People Tracking,
    Atti della Seconda Conferenza Italiana sui Sistemi Intelligenti - CISI,
    2006
    , (Seconda Conferenza Italiana sui Sistemi Intelligenti - CISI 2006,
    Ancona, Italy,
    09/27/2006 - 09/29/2006)
  7. O. Lanz; R. Brunelli,
    Dynamic Head Location and Pose from Video,
    IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2006,
    n. 4042078,
    2006
    , pp. 47-
    52
    , (IEEE Conference on Multisensor Fusion and Integration - MFI,
    Heidelberg (Germany),
    da 09/03/2006 a 09/06/2006)
  8. O. Lanz,
    Method and apparatus for tracking a number of objects or object parts in image sequence.
    2006,
    The present invention provides for a method for tracking a number of objects or object parts in image sequences, comprising following a Bayesian-like approach to object tracking, computing, at each time a new image is available, a probability distribution over all possible target configurations for that time, said Bayesian-like approach to object tracking comprising the following steps:
    - Prediction step: a probability distribution is computed for the previous image, at time (t-1), is propagated to the new image at time (t) according to a probabilistic model of target dynamics, obtaining a predicted distribution at time (t) ;
    - Update step: the predicted distribution at time (t) is then aligned with the evidence contained in the new image at time (t) according to a probabilistic model of visual likelihood.
  9. O. Lanz; R. Manduchi,
    Hybrid Joint-Separable Multibody Tracking,
    vol.1,
    2005
    , pp. 413-
    420
    , (IEEE Conference on Computer Vision and Pattern Recognition - CVPR,
    San Diego (CA),
    from 06/20/2005 to 06/26/2005)
  10. O. Lanz,
    Probabilistic Multi-Person Tracking for Ambient Intelligence,
    Monitoring human activities with visual sensors is still a challenge, especially when multiple targets are involved. Occlusions, if not properly handled, are a major source of failure. Indoor environments with complex topology require the use of sensor networks whose effective management is by itself a difficult problem. Situated in the context of Ambient Intelligence, this thesis is concerned with both algorithmic and architectural aspects of distributed monitoring systems. The algorithmic approach pursued is that of non–parametric Bayesian filtering, a probabilistic state estimation framework whose multi target formulation allows physically–based modeling of the occlusion process within an appearance based, background independent observation model. While unaffordable in its plain, joint formulation, a novel, efficient, probabilistically sound solution is proposed and its robustness experimentally verified. Its MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation within a distributed modular architecture. Representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent sensor agencies are synergically reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its own performance, environment and sensing and computing infrastructure, providing a scalable solution to the problem of visual monitoring of crowded, topologically complex environments,
    2005

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