Quickest Moving Object Detection
This work addresses the need for timely object detection in dynamic video environments, such as with moving cameras, but it appears incremental as it builds on existing Quickest Change Detection frameworks.
The paper tackles the problem of detecting and segmenting a moving object in video with minimal delay while controlling false alarms, using an online motion segmentation approach based on Quickest Change Detection. Experiments demonstrate the method's effectiveness in minimizing detection delay under false alarm constraints.
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.