Principal Components Analysis of Optical Snow

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or depth coherence can be assumed. Previous methods for measuring optical snow have used a wedge filter in a spatiotemporal frequency domain. The PCA method is also based on the spatiotemporal frequency domain analysis, but examines a different geometry property of the spectrum. We compare the results of the PCA method to the previous methods using both real and synthetic sequences.