ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 837–844, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-837-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 837–844, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-837-2020

  03 Aug 2020

03 Aug 2020

OBJECT DETECTION UNDER MOVING CLOUD SHADOWS IN WAMI

P. U. Hatipoğlu1, R. T. Albayrak1, and A. A. Alatan2 P. U. Hatipoğlu et al.
  • 1ESEN System Integration, 06800 METU Technopolis Ankara, Turkey
  • 2Dept. of Electrical & Electronics Engineering, METU, 06800, Ankara, Turkey

Keywords: Moving Cast Shadow Detection, Cloud Shadow, Background Subtraction, Moving Object Detection, Gaussian Mixture Models, Video Surveillance, Wide Area Motion Imagery

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.