A semi-supervised recurrent neural network for video salient object detection

A semi-supervised, one-dimensional recurrent neural network (RNN) approach called RVS has been proposed in this paper for video salient object detection. The proposed RVS approach involves the processing of each frame independently without explicitly considering temporal information. The RNN is trained using one-dimensional superpixel features to classify the salient object regions into salient foreground and non-salient background superpixels. Deep learning algorithms generally exhibit heavy dependence on training data size and often take extremely long time for training. On the contrary, the proposed RVS approach involves the training of an RNN using a small data which results in significant reduction in training time. The RVS approach has been extensively evaluated and its results are compared with those of several state-of-the-art methods using the public-domain VideoSeg, SegTrack v1 and SegTrack v2 benchmark video datasets. Further, the RVS approach has been tested using the authors’ own video dataset and the complex DAVIS and video object segmentation datasets to evaluate the impact of motion and blur on its performance. The RVS approach delivers results superior to those of several approaches that strongly rely upon spatio-temporal features in detecting the salient objects from the video sequences.