中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
ZBS:Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

文献类型:会议论文

作者安永琪1,2; 赵旭1,2; 于涛1,2; 郭海云1,2; 赵朝阳1,2; 唐明1,2; 王金桥1,2
出版日期2023-06
会议日期2023-6-18到2023-6-22
会议地点加拿大温哥华
英文摘要

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsupervised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://github.com/CASIA-IVA-Lab/ZBS.

源URL[http://ir.ia.ac.cn/handle/173211/51511]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
紫东太初大模型研究中心
通讯作者赵旭
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
安永琪,赵旭,于涛,等. ZBS:Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection[C]. 见:. 加拿大温哥华. 2023-6-18到2023-6-22.

入库方式: OAI收割

来源:自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。