  This paper presents a novel method to involve both spatial and temporal
features for semantic video segmentation. Current work on convolutional neural
networks(CNNs) has shown that CNNs provide advanced spatial features supporting
a very good performance of solutions for both image and video analysis,
especially for the semantic segmentation task. We investigate how involving
temporal features also has a good effect on segmenting video data. We propose a
module based on a long short-term memory (LSTM) architecture of a recurrent
neural network for interpreting the temporal characteristics of video frames
over time. Our system takes as input frames of a video and produces a
correspondingly-sized output; for segmenting the video our method combines the
use of three components: First, the regional spatial features of frames are
extracted using a CNN; then, using LSTM the temporal features are added;
finally, by deconvolving the spatio-temporal features we produce pixel-wise
predictions. Our key insight is to build spatio-temporal convolutional networks
(spatio-temporal CNNs) that have an end-to-end architecture for semantic video
segmentation. We adapted fully some known convolutional network architectures
(such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our
spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art
semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets.
