An investigation into image and video foreground segmentation and change detection - Zhang, Chao (2019)
Detecting and segmenting Spatio-temporal foreground objects from videos are significant to motion pattern modelling and video content analysis. Extensive efforts have been made in the past decades. Nevertheless, video-based saliency detection and foreground segmentation remained challenging. On the one hand, the performances of image-based saliency detection algorithms are limited in complex contents, while the temporal connectivity between frames are not well-resolved. On the other hand, compared with the prosperous image-based datasets, the datasets in video-level saliency detection and segmentation usually have smaller scale and less diversity of contents. Towards a better understanding of video-level semantics, this thesis investigates the foreground estimation and segmentation in both image-level and video-level.