Search Results for author: Christopher Reining

Found 8 papers, 2 papers with code

Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings

no code implementations23 Oct 2023 Hazem Youssef, Frederik Polachowski, Jérôme Rutinowski, Moritz Roidl, Christopher Reining

A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest.

Object Object Localization +1

Multi-Channel Time-Series Person and Soft-Biometric Identification

no code implementations4 Apr 2023 Nilah Ravi Nair, Fernando Moya Rueda, Christopher Reining, Gernot A. Fink

On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e. g., in industrial settings.

Attribute Human Activity Recognition +2

Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach

no code implementations3 Apr 2023 Jérôme Rutinowski, Hazem Youssef, Sven Franke, Irfan Fachrudin Priyanta, Frederik Polachowski, Moritz Roidl, Christopher Reining

This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e. g., pallets, crates, barrels) over a network of, in this example, six RGB cameras.

Dataset Bias in Human Activity Recognition

no code implementations19 Jan 2023 Nilah Ravi Nair, Lena Schmid, Fernando Moya Rueda, Markus Pauly, Gernot A. Fink, Christopher Reining

It is unknown what physical characteristics and/or soft-biometrics, such as age, height, and weight, need to be taken into account to train a classifier to achieve robustness towards heterogeneous populations in the training and testing data.

Human Activity Recognition Time Series +1

On the Applicability of Synthetic Data for Re-Identification

1 code implementation20 Dec 2022 Jérôme Rutinowski, Bhargav Vankayalapati, Nils Schwenzfeier, Maribel Acosta, Christopher Reining

The quality of the generated images was gauged using a perspective classifier that was trained on the original images and then applied to the synthetic ones, comparing the accuracy between the two sets of images.

Data Augmentation

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

1 code implementation9 Dec 2022 Anas Gouda, Danny Heinrich, Mirco Hünnefeld, Irfan Fachrudin Priyanta, Christopher Reining, Moritz Roidl

Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors.

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