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ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

Introduction

This repository is an official implementation of the ZBS. ZBS fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. It can detect most of the categories in the real world and can detect the unseen foreground categories outside the pre-defined categories. ZBS achieves remarkably 4.70% F-Measure improvements over state-of-the-art unsupervised methods.

Features

The first unsupervised zero-shot background subtraction.
The first background subtraction method based on an instance-level background model.
Detects any class given class names (using Detic).
State-of-the-art results on CDnet 2014 dataset compared with other unsupervised background subtraction method.

Instructions

See GET_STARTED.md.

Main Results

Overall and per-category F-Measure comparison of different Unsupervised BGS methods on the CDnet 2014 dataset.

Unsupervised BGS	baseline	camjitt	dynbg	intmot	shadow	thermal	badwea	lowfr	night	PTZ	turbul	Overall
PAWCS	0.9397	0.8137	0.8938	0.7764	0.8913	0.8324	0.8152	0.6588	0.4152	0.4615	0.6450	0.7403
SuBSENSE	0.9503	0.8152	0.8177	0.6569	0.8986	0.8171	0.8619	0.6445	0.5599	0.3476	0.7792	0.7408
WisenetMD	0.9487	0.8228	0.8376	0.7264	0.8984	0.8152	0.8616	0.6404	0.5701	0.3367	0.8304	0.7535
SWCD	0.9214	0.7411	0.8645	0.7092	0.8779	0.8581	0.8233	0.7374	0.5807	0.4545	0.7735	0.7583
SemanticBGS	0.9604	0.8388	0.9489	0.7878	0.9478	0.8219	0.8260	0.7888	0.5014	0.5673	0.6921	0.7892
RTSS	0.9597	0.8396	0.9325	0.7864	0.9551	0.8510	0.8662	0.6771	0.5295	0.5489	0.7630	0.7917
RT-SBS-v2	0.9535	0.8233	0.9217	0.8946	0.9497	0.8697	0.8279	0.7341	0.5629	0.5808	0.7315	0.8045
ZBS (Ours)	0.9653	0.9545	0.9290	0.8758	0.9765	0.8698	0.9229	0.7433	0.6800	0.8133	0.6358	0.8515

Overall and per-category result of ZBS on the CDnet 2014 dataset.

Category	Recall	Specificity	PWC	Precision	F-Measure
badWea	0.9049	0.9988	0.2755	0.9439	0.9229
baseline	0.9709	0.9988	0.2237	0.9603	0.9653
camjitt	0.9543	0.9979	0.4022	0.9554	0.9545
dynbg	0.9269	0.9996	0.0951	0.9340	0.9290
intmot	0.8254	0.9965	1.6864	0.9481	0.8758
lowfr	0.7302	0.9988	0.3279	0.7584	0.7433
night	0.6341	0.9958	1.2477	0.7666	0.6800
PTZ	0.7490	0.9997	0.2387	0.9223	0.8133
shadow	0.9712	0.9991	0.2097	0.9819	0.9765
thermal	0.8475	0.9954	1.1686	0.9040	0.8698
turbul	0.7286	0.9984	0.3198	0.6075	0.6358
Overall	0.8403	0.9981	0.5632	0.8802	0.8515

Citation

If you find this project useful for your research, please consider citing this paper.

@inproceedings{
an2023zbs,
title={{ZBS}: Zero-shot Background Subtraction via instance-level background modeling and foreground selection},
author={Yongqi An and Xu Zhao and Tao Yu and Haiyun Guo and Chaoyang Zhao and Ming Tang and Jinqiao Wang},
booktitle={Conference on Computer Vision and Pattern Recognition 2023},
year={2023},
url={https://openreview.net/forum?id=f-9UZN4GEV}
}

Acknowledgement

Our repository is mainly built upon Detic.