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Task-Driven Video Collection

dc.contributor.authorLim, Ser-Nam
dc.contributor.authorMittal, Anuarg
dc.contributor.authorDavis, Larry S.
dc.date.accessioned2006-01-23T17:16:09Z
dc.date.available2006-01-23T17:16:09Z
dc.date.issued2006-01-23T17:16:09Z
dc.identifier.urihttp://hdl.handle.net/1903/3042
dc.description.abstractVision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important that data suitable for these algorithms be acquired - a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ cameras. In this paper, we describe a multi-camera system that collects images and videos of moving objects in such scenes, subject to task constraints. The system constructs "task visibility intervals" that contain information about what can be sensed in future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Using a plane-sweep algorithm, these atomic intervals can be combined to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Although cameras can then be scheduled based on the constructed intervals, finding an optimal schedule is a typical NP-hard problem. Due to this, and the lack of exact future information in a dynamic environment, we propose several methods for fast camera scheduling that yield solutions within a small constant factor of optimal. Experimental results illustrate system capabilities for both real and more complicated simulated scenarios.en
dc.format.extent2134121 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.relation.ispartofseriesUM Computer Science Departmenten
dc.relation.ispartofseriesCS-TR-4780en
dc.relation.ispartofseriesUMIACSen
dc.relation.ispartofseriesUMIACS-TR-2006-05en
dc.titleTask-Driven Video Collectionen
dc.typeTechnical Reporten


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