Semantic Video Segmentation: A Review on Recent Approaches
It provides a survey for researchers in computer vision, summarizing existing work without introducing new methods.
This paper reviews recent approaches to semantic video segmentation, comparing various methods and highlighting the superiority of CNN-based systems on datasets like CamVid and NYUDv2.
This paper gives an overview on semantic segmentation consists of an explanation of this field, it's status and relation with other vision fundamental tasks, different datasets and common evaluation parameters that have been used by researchers. This survey also includes an overall review on a variety of recent approaches (RDF, MRF, CRF, etc.) and their advantages and challenges and shows the superiority of CNN-based semantic segmentation systems on CamVid and NYUDv2 datasets. In addition, some areas that is ideal for future work have mentioned.