The human vision system is capable of recognizing and extracting shadows from complex scenes and uses shadow information to automatically perform various tasks, such as perception of the position, size, and shape of the objects, understanding the structure of the 3D scene geometry and location andġ School of Computing, Engineering and Mathematics, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia. Shadows play an important role in our understanding of the world and provide rich visual information about the properties of objects, scenes, and lights. Keywords moving shadow detection problematic situations shadow features shadow detection methods This survey suggests the ways of selecting shadow detection methods under different scenarios. The evaluation is carried out using benchmark datasets that have been selected and modified to suit the purpose. This paper addresses the main problematic situations associated with shadows and provides a comprehensive performance comparison on up-to-date methods that have been proposed to tackle these problems. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. In common object detection systems, due to having similar characteristics, shadows can be easily misclassified as either part of moving objects or independent moving objects. This article is published with open access at Ībstract Shadows of moving objects may cause serious problems in many computer vision applications, including object tracking and object recognition. Mosin Russell1 (El), Ju Jia Zou1, and Gu Fang1 Computational Visual Media DOI 10.1007/s4109-0Īn evaluation of moving shadow detection techniques
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