  Recently, semantic video segmentation gained high attention especially for
supporting autonomous driving systems. Deep learning methods made it possible
to implement real time segmentation and object identification algorithms on
videos. However, most of the available approaches process each video frame
independently disregarding their sequential relation in time. Therefore their
results suddenly miss some of the object segments in some of the frames even if
they were detected properly in the earlier frames. Herein we propose two
sequential probabilistic video frame analysis approaches to improve the
segmentation performance of the existing algorithms. Our experiments show that
using the information of the past frames we increase the performance and
consistency of the state of the art algorithms.
