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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 Full 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; 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)
  2. 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)
  3. 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)
  4. 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
  5. O. Lanz,
    A Joint-Separable Filter for Multitarget Tracking,
    Tracking multiple targets with visual sensors is a challenging problem, especially when efficiency is an issue. Occlusions, if not properly handled, are a major source of failure. Solutions supporting principled occlusion reasoning have been proposed but are yet unpractical for online applications. This article presents a new solution which effectively manages the trade-off between reliable modeling and computational efficiency. The Hybrid Joint-Separable (HJS) filter is derived from a joint Bayesian formulation of the problem, and shown to be efficient while optimal in terms of efficient belief representation. Computational efficiency is achieved by employing a forward MRF approximation to joint dynamics and an incremental algorithm for posterior update with an appearance likelihood that implements a physically-based model of the occlusion process. Its real time MonteCarlo implementation achieves accurate tracking during partial occlusions, while in case of complete occlusion tracking hypotheses are bound to estimated occlusion volumes. Experiments show that the proposed algorithm is very robust and able to resolve even long term complete occlusions between targets with identical appearance,
    2005
  6. O. Lanz,
    Dynamic Resource Allocation for Probabilistic Tracking in Complex Scenes,
    Monitoring human activities in large environments is a challenging problem. Such scenarios impose the use of sensor networks and decentralized processing to achieve scalability in terms of spatial environment and task complexity. This article addresses both algorithmic and architectural aspects of distributed tracking systems. The algorithmic approach pursued is that of sequential Bayesian filtering, whose standard formulation is rewritten in a form suitable for distribution within a three-layered modular architecture. Its non-parametric MonteCarlo implementation provides a suitable framework for tackling dynamic resource allocation: representation size of probabilistic estimates is adapted to conveyed uncertainty, while active environment sampling aims at minimizing uncertainty. Independent tracking agencies with competence on a subset of targets are instantiated and continuously reconfigured by a supervisor process with global environment knowledge. The proposed system is then adaptive to its sensing and computing infrastructure, own performance and environment, providing a scalable solution to the problem of visual monitoring of populated, topologically complex environments,
    2005
  7. O. Lanz,
    Automatic Lens Distortion Estimation for an Active Camera,
    2004
    , pp. 575-
    580
    , (International Conference on Computer Vision and Graphics - ICCVG,
    Warsaw (Poland),
    da 09/22/2004 a 09/24/2004)
  8. O. Lanz,
    Occlusion Robust Tracking of Multiple Objects,
    Computational Imaging and Vision,
    Springer,
    vol.32,
    2004
    , pp. 715-
    720
    , (International Conference on Computer Vision and Graphics - ICCVG,
    Warsaw (Poland),
    from 09/22/2004 to 09/24/2004)
  9. O. Lanz,
    Modeling Interactions in Multiple Object Bayesian Tracking,
    This paper proposes a framework for modeling interactions in muliple object 3D Bayesian tracking. It exploits both the computational cheapness of independent single object filters and the modeling power of the joint filter. Sampling from complex joint propagation densities is avoided by introducing interaction a posteriori, after blind single object propagation has been performed. To deal with occlusions for each object a support layer is computed. It contains probabilistic information about how likely a pixel is occluded by another object. Utilized to give less weight to likely occluded pixels, it provides the basis of a robust likelihood model. The implementation of the proposed ideas in a Sequential Monte Carlo framework are discussed. Experiments on a synthetic data show the robustness of the proposed ideas,
    2004
  10. Alessandro Santuari; Oswald Lanz; Roberto Brunelli,
    Synthetic Movies for Computer Vision Applications,
    Proceedings of the Third IASTED International Conference on Visualization, Imaging, and Image Processing,
    2003
    , pp. 139-
    144

Pages