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• W2187750906 abstract "This paper deals with a real time visual surveillance system for detecting moving semantic objects of a certain class (such as humans or cars) from digital videos even in low-level illumination. Several existing systems used colour cues which is a major disadvantage in case of low-level illuminations. And in some existing systems results were not very accurate. In this method the prior identification of object is not needed whereas some existing system requires it. Here recursive algorithm is used to overcome such drawbacks. The main advantage of the proposed method is that shape analysis is used to detect objects without much noise. Both recursive algorithm and shape analysis produce very accurate results. This paper finds major application in case of security purposes. image resolution (typically lower for IR sensors than video sensors) and the number of people in its field of view. In the long run, moving object detection will be extended with models to recognize the actions of the people it tracks. People are interested in interactions between human beings and objects - e.g., people exchanging objects, leaving objects in the scene, taking objects from the scene. The descriptions of people - their global motions and the motions of their parts - developed by Moving object detection are designed to support such activity recognition. Moving object detection currently operates on video taken from a fixed camera, and many of its image analysis algorithms would not generalize easily to images taken from a moving camera. At this point, the surveillance system might stop and invoke a system like Moving object detection to verify the presence of people and recognize their actions. In Moving objects detection, foreground regions are detected in every frame by a combination of background analysis and simple low level processing of the resulting binary image. The background scene is statically modelled by the minimum and maximum intensity values and maximal temporal derivative for each pixel recorded over some period, and is updated periodically. Each foreground region is matched to the current set of objects using a combination of shape analysis and tracking. These include simple spatial occupancy overlap tests between the predicted locations of objects and the locations of detected foreground regions, and ―dynamic template matching algorithms that correlate evolving appearance models of objects with foreground regions. Second-order motion models, which combine robust techniques for region detection and matching of silhouette edges with recursive least square estimation, are used to predict the locations of objects in future frames. II. SYSTEM DESCRIPTION" @default.
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• W2187750906 date "2014-01-01" @default.
• W2187750906 modified "2023-09-27" @default.
• W2187750906 title "Moving Object Detection Using Recursive Algorithm" @default.
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