A Rough Set Theory Based Edge Detection for Moving Object Detection

Computer vision is the area of artificial intelligence which automates the machine as a human vision or even better. The main focus and the demand of the society have motivated the researchers across the world towards the intelligent and efficient smart surveillance systems. Perhaps being the necessity, moving object detection is very challenging due to various challenges such as illumination variation, adverse climatic conditions and the dynamic backgrounds. This paper describes the various challenges of object detection in the real-time scenarios and further it describes the various object detection techniques and the rough set theory. The paper demonstrates rough set theory based edge detection and background subtraction model for moving object detection to detect boundary of the objects and thus detection becomes more efficient. Proposed work can be utilized for object detection in the overlapping data and for the data where background is unavailable and difficult to identify the actual object.