The paper provides a specific perspective view on background subtraction for moving object detection, as a building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. Since color information is not sufficient for dealing with problems like light switches or local gradual changes of illumination, shadows cast by the foreground objects, and color camouflage, new information needs to be caught to deal with these issues. Depth synchronized information acquired by low-cost RGBD sensors is considered in this paper to give evidence about which issues can be solved, but also to highlight new challenges and design opportunities in several applications and research areas.
Background Subtraction for Moving Object Detection in RGBD Data: A Survey
MADDALENA, LUCIA
;Alfredo Petrosino
2018-01-01
Abstract
The paper provides a specific perspective view on background subtraction for moving object detection, as a building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. Since color information is not sufficient for dealing with problems like light switches or local gradual changes of illumination, shadows cast by the foreground objects, and color camouflage, new information needs to be caught to deal with these issues. Depth synchronized information acquired by low-cost RGBD sensors is considered in this paper to give evidence about which issues can be solved, but also to highlight new challenges and design opportunities in several applications and research areas.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
