We present a novel, training-free approach to scene change detection. Our
method leverages tracking models, which inherently perform change detection
between consecutive frames of video by identifying common objects and detecting
new or missing objects. Specifically, our method takes advantage of the change
detection effect of the tracking model by inputting reference and query images
instead of consecutive frames. Furthermore, we focus on the content gap and
style gap between two input images in change detection, and address both issues
by proposing adaptive content threshold and style bridging layers,
respectively. Finally, we extend our approach to video to exploit rich temporal
information, enhancing scene change detection performance. We compare our
approach and baseline through various experiments. While existing train-based
baseline tend to specialize only in the trained domain, our method shows
consistent performance across various domains, proving the competitiveness of
our approach.

我们提出了一种新颖的无需训练的场景变化检测方法，该方法利用跟踪模型进行连续帧变化检测，通过识别共同物体和检测新的或缺失的物体。我们还通过引入自适应内容阈值和样式桥接层解决了输入图像之间的内容差异和样式差异问题，并将该方法扩展到视频以提高场景变化检测性能。通过各种实验证明，我们的方法在不同领域上表现出一致的性能，证明了我们方法的竞争力。