Salient object detection in egocentric videos

In the realm of video salient object detection (VSOD), the majority of research has traditionally been centered on third-person perspective videos. However, this focus overlooks the unique requirements of certain first-person tasks, such as autonomous driving or robot vision. To bridge this gap, a novel dataset and a camera-based VSOD model, CaMSD, specifically designed for egocentric videos, is introduced. First, the SalEgo dataset, comprising 17,400 fully annotated frames for video salient object detection, is presented. Second, a computational model that incorporates a camera movement module is proposed, designed to emulate the patterns observed when humans view videos. Additionally, to achieve precise segmentation of a single salient object during switches between salient objects, as opposed to simultaneously segmenting two objects, a saliency enhancement module based on the Squeeze and Excitation Block is incorporated. Experimental results show that the approach outperforms other state-of-the-art methods in egocentric video salient object detection tasks. Dataset and codes can be found at . We propose a new egocentric video salient object detection (VSOD) dataset SalEgo. And we propose a new Camera Movement based method CaMSD for the new dataset and compare to some models. Experimental results show that our approach outperforms other state-of-the-art methods in egocentric video salient object detection tasks. image