Search Results for author: Jan-Nico Zaech

Found 10 papers, 3 papers with code

Adiabatic Quantum Computing for Multi Object Tracking

no code implementations CVPR 2022 Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc van Gool

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.

Association Multi-Object Tracking

Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction

no code implementations12 Aug 2021 Edoardo Mello Rella, Jan-Nico Zaech, Alexander Liniger, Luc van Gool

Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems.

Motion Forecasting Trajectory Prediction

Learnable Online Graph Representations for 3D Multi-Object Tracking

no code implementations23 Apr 2021 Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Martin Danelljan, Luc van Gool

Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality.

3D Multi-Object Tracking Association +1

Unsupervised Robust Domain Adaptation without Source Data

no code implementations26 Mar 2021 Peshal Agarwal, Danda Pani Paudel, Jan-Nico Zaech, Luc van Gool

This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data.

Image Classification Unsupervised Domain Adaptation

Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context

no code implementations29 Apr 2020 Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Luc van Gool

Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228, 000 action sequences.

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

no code implementations19 Sep 2019 Jan-Nico Zaech, Cong Gao, Bastian Bier, Russell Taylor, Andreas Maier, Nassir Navab, Mathias Unberath

Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.

Computed Tomography (CT)

Texture Underfitting for Domain Adaptation

no code implementations29 Aug 2019 Jan-Nico Zaech, Dengxin Dai, Martin Hahner, Luc van Gool

Comprehensive semantic segmentation is one of the key components for robust scene understanding and a requirement to enable autonomous driving.

Autonomous Driving Domain Adaptation +3

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

2 code implementations22 Mar 2018 Bastian Bier, Mathias Unberath, Jan-Nico Zaech, Javad Fotouhi, Mehran Armand, Greg Osgood, Nassir Navab, Andreas Maier

In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction.

Anatomy Decision Making +1

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