CVPR 2023 - ZBS: Zero-shot Background Subtraction via Instance-level Background

In this episode we discuss ZBS: Zero-shot Background Subtraction via Instance-level Background by Yongqi An, Xu Zhao, Tao Yu, Haiyun Guo, Chaoyang Zhao, Ming Tang, Jinqiao Wang. The paper presents an unsupervised background subtraction (BGS) algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method uses zero-shot object detection to build an open-vocabulary instance-level background model, which can effectively extract foreground objects by comparing detection results with the background model. ZBS performs well in sophisticated scenarios and can detect objects outside predefined categories. The experimental results show that ZBS outperforms state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is available at https://github.com/CASIA-IVA-Lab/ZBS.