Learning to Track Objects Through Unobserved Regions

Authors:
Chris Stauffer
Affiliations:
Massachusetts Institute of Technology, Cambridge, MA
Venue:
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Year:
2005

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Abstract

As tracking systems become more effective at reliably tracking multiple objects over extended periods of time within single camera views and across overlapping camera views, increasing attention is being focused on tracking objects through periods where they are not observed. This paper investigates an unsupervised hypothesis testing method for learning the characteristics of objects passing unobserved from one observed location to another. This method not only reliably determines whether objects predictably pass from one location to another without performing explicit correspondence, but it approximates the likelihood of those transitions. It is robust to non-stationary traffic processes that result from traffic lights, vehicle grouping, and other non-linear vehicle-vehicle interactions. Synthetic data allows us to test and verify our results for complex traffic situations over multiple city blocks and contrast it with previous approaches.