A spatiotemporal bidirectional network for video salient object detection using multiscale transfer learning

Video saliency prediction aims to simulate human visual attention by selecting the most pertinent and important components within a video frame or sequence. When evaluating video saliency, time and space data are essential, particularly in the presence of challenging features such as fast motion, shifting background, and nonrigid deformation. The current video saliency frameworks are highly prone to failure under the specified conditions. Moreover, it is unsuitable to perform video saliency identification by solely relying on image saliency models, disregarding the temporal information in videos. This research proposes a novel Spatiotemporal Bidirectional Network for Video Salient Object Detection using Multiscale Transfer Learning (SBMTL-Net) to solve the issue of detecting important objects in videos. The SBMTL-Net produces significant outcomes for a given sequence of frames by utilizing Multi-scale transfer learning with an encoder and decoder technique to acquire knowledge and spatially and temporally map properties. SBMTL-Net model consists of bidirectional LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), where the VGG16 (Video Geometry Group) and VGG19 are utilized for multi-scale feature extraction of the input video frames. The performance of the proposed model has been evaluated on five publically available challenging datasets DAVIS-T, SegTrack-V2, ViSal, VOS-T and DAVSOD-T for the parameters MAE, F-measure and S-measure. The experimental results show the effectiveness of the proposed model as compared with other competitive models.