Visual Analysis of Outdoor Surveillance Videos Using Principal Component Analysis

Abstract

In this paper, we investigated the application of principal component analysis (PCA) to the visualization of outdoor videos for safety and security monitoring. We analyzed videos depicting daytime airplane takeoffs and landings, nighttime airplane landings, small birds flying, and small birds resting on an elevated bridge. The extracted frames were arranged on a two-dimensional plane according to their principal component scores. The results indicate that frame placement reflects inter-frame correlations and is strongly influenced by global visual factors such as illumination conditions and the size of moving subjects. A qualitative evaluation suggests that the visualization provides an intuitive overview of frame-level variations and reduces the workload required for scene exploration without continuous playback. However, the effectiveness decreases for scenes involving small or low-contrast subjects. These findings clarify the characteristics and limitations of PCA-based visualization for outdoor surveillance videos.