Minimum Delay Object Detection From Video

TL;DR

This paper introduces a real-time, minimal delay object detection method from video streams that guarantees detection speed and accuracy by leveraging CNN detectors and Quickest Detection theory.

Contribution

It presents the first real-time solution with guaranteed minimal delay for online object detection, combining CNN detectors with Quickest Detection formulation.

Findings

Increases correct detections with minimal delay

Reduces computational cost compared to single-frame detectors

Operates effectively at 50 fps overhead

Abstract

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.