期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 20, 期 5, 页码 1787-1802出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2836399
关键词
Maritime vehicles; autonomous automobiles; background subtraction; object detection; computer vision
资金
- National Research Foundation under the CorpLab@University Scheme
This paper provides a benchmark of the performance of 23 classical and state-of-the-art background subtraction (BS) algorithms on visible range and near infrared range videos in the Singapore Maritime dataset. Importantly, our study indicates the limitations of the conventional performance evaluation criteria for maritime vision and proposes new performance evaluation criteria that is better suited to this problem. This paper provides insight into the specific challenges of BS in maritime vision. We identify four open challenges that plague BS methods in maritime scenario. These include spurious dynamics of water, wakes, ghost effect, and multiple detections. Poor recall and extremely poor precision of all the 23 methods, which have been otherwise successful for other challenging BS situations, allude to the need for new BS methods custom designed for maritime vision.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据