Semi-supervised Video Object Segmentation with Recurrent Neural Network

Object segmentation in videos has been extensively investigated recent years. However, semi-supervised object segmentation in videos is still a challenging research topic as it is hard to modeling temporal information. Most of research treats video frames independence and lost the relationship between adjacent frames. To overcome the limitation, Semi-supervised Video Object Segmentation with Recurrent Neural Network (SVOSR) has been proposed which combines convolutional gated recurrent unit (ConvGRU) to learn the temporal information between adjacent frames. The proposed method can be treated as three main parts. First, the feature extraction part is proposed to generate spatial information from adjacent frames. Second the relation part extracts temporal information from the adjacent spatial information. Thirdly, the decoder part combines the spatiotemporal information and inference the results. We put forward the relation part and design the decoder part to better segmentation. Experiments show that our method shows achievable accuracy and has the order of magnitude faster inference time compared with OSVOS and other methods based on DAVIS dataset.