Minimum Delay Object Detection From Video
It provides a real-time solution for applications requiring minimal latency in object detection from video streams, though it is incremental as it builds on existing CNN-based detectors.
The paper tackles the problem of minimizing detection delay in online video object detection while maintaining accuracy, achieving a 50 fps overhead to increase correct detections and reduce computational cost compared to single-frame detectors.
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.