  We present a new two-stage pipeline for predicting frames of traffic scenes
where relevant objects can still reliably be detected. Using a recent video
prediction network, we first generate a sequence of future frames based on past
frames. A second network then enhances these frames in order to make them
appear more realistic. This ensures the quality of the predicted frames to be
sufficient to enable accurate detection of objects, which is especially
important for autonomously driving cars. To verify this two-stage approach, we
conducted experiments on the Cityscapes dataset. For enhancing, we trained two
image-to-image translation methods based on generative adversarial networks,
one for blind motion deblurring and one for image super-resolution. All
resulting predictions were quantitatively evaluated using both traditional
metrics and a state-of-the-art object detection network showing that the
enhanced frames appear qualitatively improved. While the traditional image
comparison metrics, i.e., MSE, PSNR, and SSIM, failed to confirm this visual
impression, the object detection evaluation resembles it well. The best
performing prediction-enhancement pipeline is able to increase the average
precision values for detecting cars by about 9% for each prediction step,
compared to the non-enhanced predictions.
