A Novel Video Prediction Algorithm Based on Robust Spatiotemporal Convolutional Long Short-Term Memory (Robust-ST-ConvLSTM)

Recently, video prediction algorithms based on neural networks have become a promising research direction. Therefore, a new recurrent video prediction algorithm called “Robust Spatiotemporal Convolutional Long Short-Term Memory” (Robust-ST-ConvLSTM) is proposed in this paper. Robust-ST-ConvLSTM proposes a new internal mechanism that is able to regulate efficiently the flow of spatiotemporal information from video signals based on higher-order Convolutional-LSTM. The spatiotemporal information is carried through the entire network to optimize and control the prediction potential of the ConvLSTM cell. In addition, in traditional ConvLSTM units, cell states, that carry relevant information throughout the processing of the input sequence, are updated using only one previous hidden state, which holds information on previous data unit already seen by the network. However, our Robust-ST-ConvLSTM unit will rely on N previous hidden states, that provide temporal context for the motion in video scenes, in the cell state updating process. Experimental results further suggest that the proposed architecture can improve the state-of-the-art video prediction methods significantly on two challenging datasets, including the standard Moving MNIST dataset, and the commonly used video prediction KTH dataset, as human motion dataset.