Democratizing the visualization of 500 million webcam images

Abstract

Five years ago we reported at AIPR on a nascent project to archive images from every webcam in the world and to develop algorithms to geo-locate, calibrate, and annotate this data. This archive of many outdoor scenes (AMOS) has now grown to include 28000 live outdoor cameras and over 630 million images. This is actively being used in projects ranging from large scale environmental monitoring to characterizing how built environment changes (such as adding bike lanes in DC) affects physical activity patterns over time. But the biggest value in a very long term, widely distributed image dataset is the rich set of before data that can be analyzed to evaluate changes from unexpected or sudden events. To facilitate the analysis of these natural experiments, we build and share a collection of web-tools that support large scale, data driven exploration. In this work we discuss and motivate a visualization tool that uses PCA to find the subspace that characterizes the variations in this scene, This anomaly detection captures both imaging failures such as lens flare and also unusual situations such as street fairs, and we give initial algorithm to clusters anomalies so that they can be quickly evaluated for whether they are of interest.