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dc.contributor.authorLao, Dong
dc.contributor.authorSundaramoorthi, Ganesh
dc.date.accessioned2017-05-07T05:48:00Z
dc.date.available2017-05-07T05:48:00Z
dc.date.issued2017-01-08
dc.identifier.urihttp://hdl.handle.net/10754/623383
dc.description.abstractWe present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
dc.titleMinimum Delay Moving Object Detection
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.conference.dateJanuary 8-21 2017
dc.conference.nameWinter Enrichment Program 2017 - Poster Competition
dc.conference.locationKAUST
kaust.personLao, Dong
kaust.personSundaramoorthi, Ganesh
refterms.dateFOA2018-06-13T18:12:10Z


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