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Lao_Minimum_Delay_Moving_CVPR_2017_paper.pdf
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Accepted Manuscript
Type
Conference PaperAuthors
Lao, Dong
Sundaramoorthi, Ganesh

KAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
Date
2017-11-09Online Publication Date
2017-11-09Print Publication Date
2017-07Permanent link to this record
http://hdl.handle.net/10754/626820
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We 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.Citation
Lao D, Sundaramoorthi G (2017) Minimum Delay Moving Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2017.511.Conference/Event name
30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Additional Links
http://ieeexplore.ieee.org/document/8099994/ae974a485f413a2113503eed53cd6c53
10.1109/cvpr.2017.511