COROLA: 低ランク近似による逐次的な移動物体検出
COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
動画から移動物体を検出する従来のバッチ型低ランク近似手法を、リアルタイム・長時間処理が可能なオンライン逐次フレームワークに拡張し、背景モデル学習と移動カメラへの対応も実現した。
著者: Moein Shakeri, Hong Zhang
分類: cs.CV, cs.RO
原文アブストラクト
Extracting moving objects from a video sequence and estimating the background of each individual image are fundamental issues in many practical applications such as visual surveillance, intelligent vehicle navigation, and traffic monitoring. Recently, some methods have been proposed to detect moving objects in a video via low-rank approximation and sparse outliers where the background is modeled with the computed low-rank component of the video and the foreground objects are detected as the sparse outliers in the low-rank approximation. All of these existing methods work in a batch manner, preventing them from being applied in real time and long duration tasks. In this paper, we present an online sequential framework, namely contiguous outliers representation via online low-rank approximation (COROLA), to detect moving objects and learn the background model at the same time. We also show that our model can detect moving objects with a moving camera. Our experimental evaluation uses simulated data and real public datasets and demonstrates the superior performance of COROLA in terms of both accuracy and execution time.
