SRFCNM: Spatiotemporal recurrent fully convolutional network model for salient object detection

Video saliency detection has recently been widely used because of its ability to distinguish significant regions of interest. It has several applications, such as video segmentation, abnormal activity detection, video summarization, etc. This research paper develops a novel technique for video saliency detection known as Spatiotemporal Recurrent Fully Convolutional Network Model (SRFCNM). This model uses recurrent convolutional layers to represent spatial and temporal features of superpixels for element uniqueness. The model is trained in two phases; initially, we pre-train the model on the segmented data sets and then fine-tune it for saliency detection, which allows the network to learn salient objects more accurately. The uniqueness of integrating saliency maps with recurrent convolutional layers and spatiotemporal characteristics facilitates the robust representation of salient objects to capture the relevant features. The SRFCNM model is extensively estimated on the challenging datasets viz. SegTrackV2, FBMS and DAVIS. Our model is compared with the existing Deep Learning and Convolutional Neural Network algorithms. This research demonstrates that SRFCNM outperforms the state-of-the-art saliency models considerably over the three public datasets regarding accuracy recall and mean absolute error (MAE). The proposed SRFCNM model produces the lowest MAE values, 3.2%, 3.5%, and 7.5%, for SegTrackV2, DAVIS, and FBMS datasets, respectively, with hand-crafted color features, compared with the existing models.