Search Results for author: Roberto Henschel

Found 8 papers, 3 papers with code

LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

no code implementations CVPR 2022 Duy M. H. Nguyen, Roberto Henschel, Bodo Rosenhahn, Daniel Sonntag, Paul Swoboda

Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance in crowded scenes or in wide spaces.

Multi-Object Tracking Multiple Object Tracking

Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths

2 code implementations ICCV 2021 Andrea Hornakova, Timo Kaiser, Paul Swoboda, Michal Rolinek, Bodo Rosenhahn, Roberto Henschel

We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).

Multiple Object Tracking

Lifted Disjoint Paths with Application in Multiple Object Tracking

1 code implementation ICML 2020 Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda

We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors.

Multiple Object Tracking

Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

no code implementations ECCV 2018 Timo von Marcard, Roberto Henschel, Michael J. Black, Bodo Rosenhahn, Gerard Pons-Moll

In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild.

3D Pose Estimation

Fusion of Head and Full-Body Detectors for Multi-Object Tracking

no code implementations23 May 2017 Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach.

Multi-Object Tracking

Tracking with multi-level features

no code implementations25 Jul 2016 Roberto Henschel, Laura Leal-Taixé, Bodo Rosenhahn, Konrad Schindler

We present a novel formulation of the multiple object tracking problem which integrates low and mid-level features.

Clustering Multiple Object Tracking

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