Abstract
Abstract. For a reliable and robust moving object detection, the subtraction of a precisely modeled background is crucial in wide-area motion imagery (WAMI). Even the most successful background subtraction algorithms that are designed to model highly-dynamic environments cannot cope with rapidly changing scenery, such as moving cloud shadows, which has different characteristics from dynamic textures. This paper presents a novel method to detect moving objects and to eliminate false alarms under moving cloud shadow regions in gray-level video sequences. The proposed method uses the relation between reflectance values of the shadowed and well-illuminated sequences of the regions in the video frame. A modified adaptive region growing approach, which extends from seed points, is designed to obtain the moving parts of the cloud shadows without presuming the geometric structure of the clouds. In order to determine the moving border of the cloud shadows, where false alarms typically occur, the cloud shadow motion should be detected. As the last stage of the proposed method, real moving objects in the scene are tried to be discriminated from false alarms by exploiting the relation of intensity ratios between the object candidate and its surroundings. The accuracy and computational efficiency of the proposed approach make it a reliable and feasible approach to be used in real-time surveillance solutions.
Highlights
Moving object detection and tracking are constantly developing active research areas in remote sensing and computer vision
The main contribution of this study is that moving parts of the cloud regions have been identified using adaptive double thresholding methods and the false alarms generated by the cast cloud shadow have been eliminated
The border regions of the cloud shadow cause the production of highly error-prone and misleading outputs due to the deficiencies of the background subtraction algorithms
Summary
Moving object detection and tracking are constantly developing active research areas in remote sensing and computer vision. The moving part of the cast shadow of the clouds has tried to be detected by using low fps-rate monochromatic large scale video sequences without using any prior location information of camera and cloud regions. The main contribution of this study is that moving parts of the cloud regions have been identified using adaptive double thresholding methods and the false alarms generated by the cast cloud shadow have been eliminated. In the first subsection the system overview, in the second one the assumptions of the proposed algorithm, in the third subsection the methodology to find the moving parts of the cloud shadow, and in the last subsection, the elimination method of false alarms generated by the cloud cast shadow is clarified. In the last section, the study is concluded with discussions
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