CASIA OpenIR  > 紫东太初大模型研究中心  > 图像与视频分析
ZBS:Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
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
2023-06
Conference NameCVPR
Conference Date2023-6-18到2023-6-22
Conference Place加拿大温哥华
Abstract

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.

Indexed ByEI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51511
Collection紫东太初大模型研究中心_图像与视频分析
紫东太初大模型研究中心
Corresponding Author赵旭
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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],2023.
Files in This Item: Download All
File Name/Size DocType Version Access License
2303.14679.pdf(4738KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[安永琪]'s Articles
[赵旭]'s Articles
[于涛]'s Articles
Baidu academic
Similar articles in Baidu academic
[安永琪]'s Articles
[赵旭]'s Articles
[于涛]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[安永琪]'s Articles
[赵旭]'s Articles
[于涛]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2303.14679.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.