基于深度神经网络的视频显著目标检测综述OA北大核心CSTPCD

Video salient object detection is one of the widely studied research directions in the field of computer vision,which aims to locate and segment the most salient objects or regions in video.The existing video salient object detection methods mainly extract spatiotemporal features from dynamic video sequences for saliency prediction by constructing deep neural networks.A comprehensive review of video salient object detection methods based on deep learning is con-ducted.Firstly,the basic concepts and application scenarios of video salient object detection are elaborated.Secondly,the video salient object detection methods based on deep learning are classified,and analyzed and discussed in depth by cate-gory.Subsequently,authoritative benchmark test datasets and evaluation metrics in the field of video salient object detec-tion are introduced,and quantitative and qualitative experimental comparative analysis and discussion are conducted on the most advanced models on these benchmark datasets.Finally,the challenges faced by video salient object detection are summarized,and its future development directions are discussed.