Zero-shot Video Change Detection for Real-life Industrial Applications

Change detection is crucial for various industrial applications. Although image change detection datasets are abundant, the collection of labeled video data is time-consuming, expensive, and cumbersome. This scarcity of labeled data motivates the development of few-shot or zero-shot video change detection techniques which may generalize well to new situations. Existing video change detection methods require large amounts of labeled data, are task-specific, and difficult to generalize. Therefore, in this paper, we propose a zero-shot video change detection algorithm using pre-trained deep learning models and conventional image processing techniques. Our approach identifies matching frames from input videos, adjusts lighting conditions if necessary, and uses an existing object detection model to identify objects in both frames. The method is easily generalizable by making few changes. We evaluate our proposed method on the VDAO dataset collected in a cluttered industrial environment and demonstrate its effectiveness in detecting changes between pairs of videos containing single and multiple objects.