LOST: Longterm Observation of Scenes (with Tracks)

Austin Abrams, Jim Tucek, Joshua Little, Nathan Jacobs, Robert Pless

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations


We introduce the Longterm Observation of Scenes (with Tracks) dataset. This dataset comprises videos taken from streaming outdoor webcams, capturing the same half hour, each day, for over a year. LOST contains rich metadata, including geolocation, day-by-day weather annotation, object detections, and tracking results. We believe that sharing this dataset opens opportunities for computer vision research involving very long-term outdoor surveillance, robust anomaly detection, and scene analysis methods based on trajectories. Efficient analysis of changes in behavior in a scene at very long time scale requires features that summarize large amounts of trajectory data in an economical way. We describe a trajectory clustering algorithm and aggregate statistics about these exemplars through time and show that these statistics exhibit strong correlations with external meta-data, such as weather signals and day of the week.

Original languageEnglish
Title of host publication2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
Number of pages8
StatePublished - 2012
Event2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012 - Breckenridge, CO, United States
Duration: Jan 9 2012Jan 11 2012

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986


Conference2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
Country/TerritoryUnited States
CityBreckenridge, CO

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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