Future Semantic Segmentation with Convolutional LSTM

TL;DR

This paper introduces a convolutional LSTM-based model for predicting future semantic segmentation in videos, enhancing real-time decision-making applications like autonomous driving by capturing spatiotemporal information.

Contribution

The paper presents a novel ConvLSTM model for future semantic segmentation and extends it with bidirectional ConvLSTM to improve temporal context understanding.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively captures spatiotemporal information for future frame prediction

Bidirectional ConvLSTM improves prediction accuracy

Abstract

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.