Showing papers by "Gangolf Hirtz published in 2021"
16 Apr 2021
TL;DR: OmniFlow as mentioned in this paper is a synthetic omnidirectional human optical flow dataset based on a rendering engine that creates a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during data capturing process.
Abstract: Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://mytuc.org/byfs
10 citations
01 Jan 2021
TL;DR: A concept aiming at boosting the adoption of AI and robotic related technologies to ensure sustainable, patient-centred care in hospitals is proposed, which will provide a continuous and safe monitoring of patients in the whole hospital environment from entrance to the ward.
Abstract: The current coronavirus pandemic has highlighted the need for enhanced digital technologies to provide high quality care to patients in hospitals while protecting the health and safety of the medical staff. It can also be expected that there will be a second and third wave in the corona pandemic and that preparation for future pandemics must be made. In order to close this emerging gap, we propose a concept aiming at boosting the adoption of AI and robotic related technologies to ensure sustainable, patient-centred care in hospitals. The planned assistance system will provide a continuous and safe monitoring of patients in the whole hospital environment from entrance to the ward, including data security and protection. The benefits consist in a fast detection of possible infected persons, a continuous monitoring of patients, a support by robots to reduce physical contacts during epidemics, and an automatic disinfection by robots. In addition to the technical challenges, medical, social and economic challenges for such an assistance system are discussed.
26 May 2021
TL;DR: In this paper, the authors present the simulation of a full bandwidth OFDMA system, based on a channel which is characterized by using the measurement data, and some discussions and results for low bandwidth receiver class are also presented.
Abstract: For the system performance of an OFDM-based bus system in an automotive environment, the communication channel plays a very important role. To characterize the complete system, a precise measurement of the OFDM channel is required. Further, the allocation of frequency bands for different user nodes as per their requirements opens the possibility to consider different transceiver classes optimizing the utilization of communication links. This work presents the simulation of a full bandwidth OFDMA system, based on a channel which is characterized by using the measurement data. Some discussions and results for low bandwidth receiver class are also presented.
Posted Content•
TL;DR: OmniFlow as discussed by the authors is a synthetic omnidirectional human optical flow dataset based on a rendering engine, which creates a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during data capturing process.
Abstract: Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: this https URL