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, leveraging rich temporal information to enhance the performance of scene change detection. 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.

This paper proposes an effective hypothesis generation for detection multi-vehicle using a monocular camera fixed on the host vehicle. In hypothesis generation (HG) step, we use linear model between the distance and vehicle size by using recursive least square. It generates effective image patches and improves the detection performance. In addition, it also reduces the computation time compared with sliding-window approach. In hypothesis verification (HV) step, we use the Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM). In our experiment, Caltech and IR datasets are used. The experimental result shows the improvement of running time and detection performance.