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Linear Decompositions of Long Term Time-Lapse Imagery Are Surprisingly Informative

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Robert Pless, Washington University in St. Louis

  • Computer Science Seminar
When Tue, May 06, 2014
from 11:00 AM to 11:50 AM
Where TBA
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Part of the 2014 Case Lecture.

The web has an enormous collection of live cameras view image parks, roads, cities, beaches, mountains, ski-resorts, buildings and more. Over the last 5 years, I have been archiving imagery from most (>25000) publically available outdoor cameras, and working to understand how to effectively use this massively distributed resource as a tool for phenology, environmental and atmospheric measurement.

Because these cameras are fixed in place and watch the same scene over time, this set of images is highly structured.  Because of this structure, simple algorithms based on PCA can be used for diverse purposes such as geo-locating where in the world a camera is, solving for the 3D structure of the scene in view, or segmenting the scene into component parts.  With extra information for each image such as the wind direction or sun direction, then methods based on Canonical Correlation Analysis can solve for the relative orientation of the camera, and the surface normal of parts of the scene.  I will conclude with some of the challenges we continue to face in working with these half-a-billion images.

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