Exploratory analysis of time-lapse imagery with fast subset PCA

Abstract

In surveillance and environmental monitoring applications, it is common to have millions of images of a particular scene. While there exist tools to find particular events, anomalies, human actions and behaviors, there has been little investigation of tools which allow more exploratory searches in the data. This paper proposes modifications to PCA that enable users to quickly recompute low-rank decompositions for select spatial and temporal subsets of the data. This process returns decompositions orders of magnitude faster than general PCA and are close to optimal in terms of reconstruction error. We show examples of real exploratory data analysis across several applications, including an interactive web application.