TY - GEN
T1 - Adventures in archiving and using three years of webcam images
AU - Jacobs, Nathan
AU - Burgin, Walker
AU - Speyer, Richard
AU - Ross, David
AU - Pless, Robert
PY - 2009
Y1 - 2009
N2 - Recent descriptions of algorithms applied to images archived from webcams tend to underplay the challenges in working with large data sets acquired from uncontrolled webcams in real environments. In building a database of images captured from 1000 webcams, every 30 minutes for the last 3 years, we observe that these cameras have a wide variety of failure modes. This paper details steps we have taken to make this dataset more easily useful to the research community, including (a) tools for finding stable temporal segments, and stabilizing images when the camera is nearly stable, (b) visualization tools to quickly summarize a years worth of image data from one camera and to give a set of exemplars that highlight anomalies within the scene, and (c) integration with Label Me, allowing labels of static features in one image of a scene to propagate to the thousands of other images of that scene. We also present proof-of-concept algorithms showing how this data conditioning supports several problems in inferring properties of the scene from image data.
AB - Recent descriptions of algorithms applied to images archived from webcams tend to underplay the challenges in working with large data sets acquired from uncontrolled webcams in real environments. In building a database of images captured from 1000 webcams, every 30 minutes for the last 3 years, we observe that these cameras have a wide variety of failure modes. This paper details steps we have taken to make this dataset more easily useful to the research community, including (a) tools for finding stable temporal segments, and stabilizing images when the camera is nearly stable, (b) visualization tools to quickly summarize a years worth of image data from one camera and to give a set of exemplars that highlight anomalies within the scene, and (c) integration with Label Me, allowing labels of static features in one image of a scene to propagate to the thousands of other images of that scene. We also present proof-of-concept algorithms showing how this data conditioning supports several problems in inferring properties of the scene from image data.
UR - http://www.scopus.com/inward/record.url?scp=70449572259&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449572259&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2009.5204185
DO - 10.1109/CVPR.2009.5204185
M3 - Conference contribution
AN - SCOPUS:70449572259
SN - 9781424439911
T3 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
SP - 39
EP - 46
BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
T2 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Y2 - 20 June 2009 through 25 June 2009
ER -