Quickest Moving Object Detection

TL;DR

This paper introduces a real-time, online framework for detecting and segmenting moving objects in videos with dynamic backgrounds, aiming for minimal delay while controlling false alarms.

Contribution

It presents a novel quickest change detection-based method for simultaneous detection and segmentation of moving objects in challenging video scenarios.

Findings

Effective in minimizing detection delay

Operates under dynamic backgrounds

Maintains low false alarm rate

Abstract

We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in the video. The method is an online approach based on motion segmentation, and it operates under dynamic backgrounds caused by a moving camera or moving nuisances. The goal of the method is to detect and segment the object as soon as it moves. Due to stochastic variability in the video and unreliability of the motion signal, several frames are needed to reliably detect the object. The method is designed to detect and segment with minimum delay subject to a constraint on the false alarm rate. The method is derived as a problem of Quickest Change Detection. Experiments on a dataset show the effectiveness of our method in minimizing detection delay subject to false alarm constraints.