Search Results for author: Konrad Doll

Found 12 papers, 1 papers with code

The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset

1 code implementation12 Jul 2023 Manuel Hetzel, Hannes Reichert, Günther Reitberger, Erich Fuchs, Konrad Doll, Bernhard Sick

In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data.

Scene Understanding

Sensor Equivariance by LiDAR Projection Images

no code implementations29 Apr 2023 Hannes Reichert, Manuel Hetzel, Steven Schreck, Konrad Doll, Bernhard Sick

This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric properties due to variations in sensor resolution and field of view.

Instance Segmentation Semantic Segmentation

Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information

no code implementations30 Jun 2021 Stefan Zernetsch, Oliver Trupp, Viktor Kress, Konrad Doll, Bernhard Sick

This article presents a novel approach to incorporate visual cues from video-data from a wide-angle stereo camera system mounted at an urban intersection into the forecast of cyclist trajectories.

Optical Flow Estimation

Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories

no code implementations4 Jun 2021 Viktor Kress, Fabian Jeske, Stefan Zernetsch, Konrad Doll, Bernhard Sick

We compare our method with an approach that provides forecasts in the form of Gaussian distributions and discuss the respective advantages and disadvantages.

Trajectory Forecasting

Cyclist Intention Detection: A Probabilistic Approach

no code implementations19 Apr 2021 Stefan Zernetsch, Hannes Reichert, Viktor Kress, Konrad Doll, Bernhard Sick

A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state.

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence

no code implementations11 Sep 2018 Maarten Bieshaar, Günther Reitberger, Stefan Zernetsch, Bernhard Sick, Erich Fuchs, Konrad Doll

Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e. g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties.

Activity Recognition Intent Detection

Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble

no code implementations9 Mar 2018 Maarten Bieshaar, Stefan Zernetsch, Andreas Hubert, Bernhard Sick, Konrad Doll

In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection.

Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning

no code implementations9 Mar 2018 Michael Goldhammer, Sebastian Köhler, Stefan Zernetsch, Konrad Doll, Bernhard Sick, Klaus Dietmayer

Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification.

BIG-bench Machine Learning General Classification +1

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

no code implementations6 Mar 2018 Stefan Zernetsch, Viktor Kress, Bernhard Sick, Konrad Doll

The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks.

General Classification Image Classification

Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure

no code implementations6 Mar 2018 Günther Reitberger, Stefan Zernetsch, Maarten Bieshaar, Bernhard Sick, Konrad Doll, Erich Fuchs

We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system.

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