Minimum Delay Object Detection From Video

Majed A. Alzahrani, Ganesh Sundaramoorthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We consider the problem of detecting objects, as they come into view, from videos in an online fashion. We provide the first real-time solution that is guaranteed to minimize the delay, i.e., the time between when the object comes in view and the declared detection time, subject to acceptable levels of detection accuracy. The method leverages modern CNN-based object detectors that operate on a single frame, to aggregate detection results over frames to provide reliable detection at a rate, specified by the user, in guaranteed minimal delay. To do this, we formulate the problem as a Quickest Detection problem, which provides the aforementioned guarantees. We derive our algorithms from this theory. We show in experiments, that with an overhead of just 50 fps, we can increase the number of correct detections and decrease the overall computational cost compared to running a modern single-frame detector.
Original languageEnglish (US)
Title of host publication2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Number of pages10
ISBN (Print)9781728148038
StatePublished - 2019


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