26 citations · 34 across the 2 of their papers we have counts for
5 papers
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes
Jingrui Yu, Roman Seidel, Gangolf Hirtz
We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competi…
OmniFlow: Human Omnidirectional Optical Flow
Roman Seidel, André Apitzsch, Gangolf Hirtz
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,…
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Tobias Scheck, Roman Seidel, Gangolf Hirtz
Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this…
Cubes3D: Neural Network based Optical Flow in Omnidirectional Image Scenes
André Apitzsch, Roman Seidel, Gangolf Hirtz
Optical flow estimation with convolutional neural networks (CNNs) has recently solved various tasks of computer vision successfully. In this paper we adapt a state-of-the-art appro…
Improved Person Detection on Omnidirectional Images with Non-maxima Suppression
Roman Seidel, André Apitzsch, Gangolf Hirtz
We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection…