Combining Spatiotemporal Background Modeling and Random Forest Classifier for Foreground Segmentation and Shadow Removal
Date Issued
2014
Date
2014
Author(s)
Liao, Wei-Jie
Abstract
Cast shadows detection and removal is indispensable in the object detection to many surveillance applications. In this paper, we present a novel framework for removing cast shadow of moving objects. Two main components, moving objects detector and redundant shadow remover, are integrated. For moving objects, we adopt the spatiotemporal background extractor (SBE) to detect the moving objects which is comprised of the background extractor (BE) and the background gradient extractor (BGE). SBE features the object detection in the dynamic background and the sudden lighting changes environment. For shadow removal, we use the classifier, Random Forest, to learn the shadow features, which are chromaticity, physical properties, and texture. Then, we remove the shadow from the result of SBE with the shadow classifier. The proposed method can effectively detect the moving objects and remove the shadow effect. Furthermore, we demonstrate the performance of our method compared with some state-of-the-art techniques of object detection and shadow removal.
Subjects
前景物偵測
影子消除
支持向量機
隨機森林
Type
thesis
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