Abstract. In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos. This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and strong occlusion. We show that the proposed method clearly outperforms simpler baselines both qualitatively and quantitatively.

Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the original image style transfer technique by Gatys et al. based on energy minimization. We introduce new ways of initialization and new loss functions to generate consistent and stable stylized video sequences even in cases with large motion and strong occlusion. Our second approach formulates video stylization as a learning problem. We propose a deep network architecture and training procedures that allow us to stylize arbitrary-length videos in a consistent and stable way, and nearly in real time. We show that the proposed methods clearly outperform simpler baselines both qualitatively and quantitatively. Finally, we propose a way to adapt these approaches also to 360 degree images and videos as they emerge with recent virtual reality hardware.

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Abstmct-Increasing traffle on highways leads to more dangerous situations during lane changes. We propose a s y c tem which helps the driver avoiding some of these critical situations. The Htghwny Lane Change Assistant monitors the area behind and beside the own vehicle by vision "d radar sensors. If a dangerous object is detected in the neighbour-'ing lane, a warning is issued on e lane change. The system was tested by different drivers in different vehicles on German highways and showed reasonable, accepted warnings.K e p o d -Driver assistance system, lane change, multisensor fusion, vision system, radar, lane detection