7 citations · 8 across the 3 of their papers we have counts for
5 papers
Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation
Andreas Pfeuffer, Klaus Dietmayer
Computer vision tasks such as semantic segmentation perform very well in good weather conditions, but if the weather turns bad, they have problems to achieve this performance in th…
Separable Convolutional LSTMs for Faster Video Segmentation
Andreas Pfeuffer, Klaus Dietmayer
Semantic Segmentation is an important module for autonomous robots such as self-driving cars. The advantage of video segmentation approaches compared to single image segmentation i…
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
Andreas Pfeuffer, Klaus Dietmayer
A robust and reliable semantic segmentation in adverse weather conditions is very important for autonomous cars, but most state-of-the-art approaches only achieve high accuracy rat…
Semantic Segmentation of Video Sequences with Convolutional LSTMs
Andreas Pfeuffer, Karina Schulz, Klaus Dietmayer
Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the v…
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions
Andreas Pfeuffer, Klaus Dietmayer
A good and robust sensor data fusion in diverse weather conditions is a quite challenging task. There are several fusion architectures in the literature, e.g. the sensor data can b…