Key Instance Selection for Unsupervised Video Object Segmentation

Autor:	Cho, Donghyeon, Hong, Sungeun, Kang, Sungil, Kim, Jiwon
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their similarity until the M-th frame and then assign them unique IDs (i.e., instances). Similarity measure takes into account multiple properties such as ReID descriptor, expected trajectory, and semantic co-segmentation result. After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames. Thanks to these technical contributions, our results are ranked third on the leaderboard of UVOS DAVIS challenge. Comment: Ranked 3rd in 'Unsupervised DAVIS Challenge' (CVPR 2019)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.07851 Zobrazit plný text záznamu View this record from Arxiv