  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 is that temporal image information is considered, and
their performance increases due to this. Hence, single image segmentation
approaches are extended by recurrent units such as convolutional LSTM
(convLSTM) cells, which are placed at suitable positions in the basic network
architecture. However, a major critique of video segmentation approaches based
on recurrent neural networks is their large parameter count and their
computational complexity, and so, their inference time of one video frame takes
up to 66 percent longer than their basic version. Inspired by the success of
the spatial and depthwise separable convolutional neural networks, we
generalize these techniques for convLSTMs in this work, so that the number of
parameters and the required FLOPs are reduced significantly. Experiments on
different datasets show that the segmentation approaches using the proposed,
modified convLSTM cells achieve similar or slightly worse accuracy, but are up
to 15 percent faster on a GPU than the ones using the standard convLSTM cells.
Furthermore, a new evaluation metric is introduced, which measures the amount
of flickering pixels in the segmented video sequence.
