Recently, there has been an upsurge in the research on maritime vision, where
a lot of works are influenced by the application of computer vision for
Unmanned Surface Vehicles (USVs). Various sensor modalities such as camera,
radar, and lidar have been used to perform tasks such as object detection,
segmentation, object tracking, and motion planning. A large subset of this
research is focused on the video analysis, since most of the current vessel
fleets contain the camera's onboard for various surveillance tasks. Due to the
vast abundance of the video data, video scene change detection is an initial
and crucial stage for scene understanding of USVs. This paper outlines our
approach to detect dynamic scene changes in USVs. To the best of our
understanding, this work represents the first investigation of scene change
detection in the maritime vision application. Our objective is to identify
significant changes in the dynamic scenes of maritime video data, particularly
those scenes that exhibit a high degree of resemblance. In our system for
dynamic scene change detection, we propose completely unsupervised learning
method. In contrast to earlier studies, we utilize a modified cutting-edge
generative picture model called VQ-VAE-2 to train on multiple marine datasets,
aiming to enhance the feature extraction. Next, we introduce our innovative
similarity scoring technique for directly calculating the level of similarity
in a sequence of consecutive frames by utilizing grid calculation on retrieved
features. The experiments were conducted using a nautical video dataset called
RoboWhaler to showcase the efficient performance of our technique.

我们的研究主要探讨了海洋视觉中的场景变化检测，在这项工作中，我们利用计算机视觉技术结合非监督学习的方法，通过使用多个海洋数据集进行模型训练，提出了一种基于相似度评分的动态场景变化检测技术，实验证明了其高效的性能。