SEF-VSOD: A Static Ensemble Framework For Video Saliency Using Modified Doubly U-Net

In this digital age, video and images are commonly used to process information. Although an image or video contains a lot of information, not all of it is useful. One of the key areas of computer vision is salient object detection, whose major goal is to replicate the human visual system and recognize the scene's most prominent object. The salient object detection techniques have a significant impact on a wide range of applications, including video summarization, automated cropping, etc. Deep learning approaches have recently become more common in the saliency detection problem. However, it is highly challenging for a single model to perform well in all circumstances due to the different constraints in the model creation process (training, parameter adjustment). To address this issue, the author(s) present a static ensemble framework for estimating video saliency. This ensemble approach aided in model performance improvement. The proposed model's performance is evaluated using two well-known publicly available video datasets, namely ViSal and DAVSOD-Easy. Based on the various quantitative evaluation parameters the proposed model performance is compared with the state-of- the-art SOD models.