  We consider the problem of predicting semantic segmentation of future frames
in a video. Given several observed frames in a video, our goal is to predict
the semantic segmentation map of future frames that are not yet observed. A
reliable solution to this problem is useful in many applications that require
real-time decision making, such as autonomous driving. We propose a novel model
that uses convolutional LSTM (ConvLSTM) to encode the spatiotemporal
information of observed frames for future prediction. We also extend our model
to use bidirectional ConvLSTM to capture temporal information in both
directions. Our proposed approach outperforms other state-of-the-art methods on
the benchmark dataset.
