Video Instance Segmentation 2019: A winning approach for combined Detection, Segmentation, Classification and Tracking

被引：11
|
作者：
Luiten, Jonathon [1 ,2 ]
Torr, Philip H. S. [2 ]
Leibe, Bastian [1 ]
机构：
[1] Rhein Westfal TH Aachen, Aachen, Germany
[2] Univ Oxford, Oxford, England
来源：
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) | 2019年
基金：
英国工程与自然科学研究理事会;
关键词：
D O I：
10.1109/ICCVW.2019.00088
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Video Instance Segmentation (VIS) is the task of localizing all objects in a video, segmenting them, tracking them throughout the video and classifying them into a set of pre-defined classes. In this work, divide VIS into these four parts: detection, segmentation, tracking and. classification. We then develop algorithms for petforming each of these four sub tasks individually, and combine these into a complete solution for VIS. Our solution is an adaptation of Un-OVOST, the current best petforming algorithm for Unsupervised Video Object Segmentation, to this VIS task. We benchmark our algorithm on the 2019 YouTube-VIS Challenge, where we obtain first place with an mAP score of 46.7%.
引用
收藏
页码：709 / 712
页数：4
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