Moving Object Detection System

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1) Moving Object Detection It is the low-level application for video surveillance system. The object detection system consists of two major components: 1)Background modeling;2) Motion/Object segmentation. Background modeling is the representation of the scene without moving objects and must be regularly updated. Motion segmentation aims at detecting regions corresponding to moving objects such as humans or vehicles. Knowing the exact number of persons in a building can be critical for the success of business or rescue operations. Moving object counting applications are used to count the number of persons entering or leaving the entrance of building [3], estimate the…show more content…
Tracking techniques have been studied extensively for such purposes as video content analysis as well as remote surveillance. They greatly depend on motion and object detection stage. Moving objects are temporally tracked from one frame to another by features-based matching such as shape, blob, points, etc [7]. It can be 2D, from a single camer a, or 3D by combining two views with known geometric relationships. Moving object tracking is useful for many applications such as traffic congestion monitoring [8], abnormal event detection [9], fight detection, abandoned or stolen objects [10], elderly person monitoring in indoor environment [11], Detection of presence in a forbidden area, car parking and sudden stopped object, etc. In object tracking , occlusions significantly undermine the performance of tracking algorithms. 3) Moving Object Classification and identification The objects detected by a video surveillance system are usually classified into various categories: human,…show more content…
Reading license plates from a video surveillance is a difficult application [14]. It requires a high resolution image. To maximize efficiency, plate recognition is most often done through specialized systems with well- positioned cameras and adequate lighting quality. 4) Moving Object Behavioral analysis and understanding Behavior analysis and understanding is considered as the senior-level video surveillance application. This is an essential step in which the information from the low-level and intermediate-level tasks are interpreted by a semantic high- level description. One of the objectives of the surveillance is to interpret the behaviors of individuals and objects in a scene and their interactions, then describe them with natural language. In a broad sense, behaviors are defined as observable actions. In video surveillance systems, recognition events generally depends on the context of the scene, the same behavior can have different meanings depending on the environment and context analysis. As the behavior recognition task sometimes requires a complex semantic analysis, of what appears in

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