KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Electrical Engineering Program
Visual Computing Center (VCC)
King Abdullah University of Science & Technology (KAUST), Saudi Arabia
Permanent link to this recordhttp://hdl.handle.net/10754/626820
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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.
CitationLao 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 name30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)