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We consider the problem of predicting semantic segmentation of future framesin a video. Given several observed frames in a video, our goal is to predictthe semantic segmentation map of future frames that are not yet observed. Areliable solution to this problem is useful in many applications that requirereal-time decision making, such as autonomous driving. We propose a novel modelthat uses convolutional LSTM (ConvLSTM) to encode the spatiotemporalinformation of observed frames for future prediction. We also extend our modelto use bidirectional ConvLSTM to capture temporal information in bothdirections. Our proposed approach outperforms other state-of-the-art methods onthe benchmark dataset.

Future Semantic Segmentation with Convolutional LSTM