文件名称：Feature Space Optimization for Semantic Video Segmentation
文件大小：2.18MB
文件格式：PDF
更新时间：2020-01-23 07:03:35
Computer Vision, Semantic Segmentation
We present an approach to long-range spatio-temporal regularization in semantic video segmentation. Temporal regularization in video is challenging because both the camera and the scene may be in motion. Thus Euclidean distance in the space-time volume is not a good proxy for correspondence. We optimize the mapping of pixels to a Euclidean feature space so as to minimize distances between corresponding points. Structured prediction is performed by a dense CRF that operates on the optimized features. Experimental results demonstrate that the presented approach increases the accuracy and temporal consistency of semantic video segmentation.