Publication Date: 6/18/2018
Event: Conference on Computer Vision and Pattern Recognition (CVPR) 2018, Salt Lake City, UT USA
Reference: pp 7415-7424, 2018
Authors: Jingchun Cheng, Tsinghua University, China, University of California, Merced; Yi-Hsuan Tsai, University of California, Merced, NEC Laboratories America, Inc.; Wei-Chih Hung, University of California, Merced; Shengjin Wang, Tsinghua University, China; Ming-Hsuan Yang, University of California, Merced, NEC Laboratories America, Inc.
Abstract: Online video object segmentation is a challenging task as it entails to process the image sequence timely and accurately. To segment a target object through the video, numerous CNN-based methods have been developed by heavily finetuning on the object mask in the first frame, which is time-consuming for online applications. In this paper, we propose a fast and accurate video object segmentation algorithm that can immediately start the segmentation process once receiving the images. We first utilize a part-based tracking method to deal with challenging factors such as large deformation, occlusion, and cluttered background. Based on the tracked bounding boxes of parts, we construct a region-of-interest segmentation network to generate part masks. Finally, a similarity-based scoring function is adopted to refine these object parts by comparing them to the visual information in the first frame. Our method performs favorably against state-of-the-art algorithms in accuracy on the DAVIS benchmark dataset, while achieving much faster runtime performance.