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

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