Learning Temporal Features With Cnns For Monocular Visual Ego Motion Estimation

Making Convolutional Neural Networks (CNNs) successful in learning problems like image based ego motion estimation, highly depends on the ability of the network to extract the temporal information from videos. Therefore, the architecture of a network needs the capability to learn temporal features.We propose two CNN architectures which are able to learn features for the extraction of this temporal information and are able to solve problems like ego motion estimation. Our architectures achieve first promising results in ego motion estimation and might be a good foundation for systems dealing with temporal information. As the architectures reach real time inference time, they can be applied in domains like autonomous driving.