الفهرس 
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Abstract
The area of automated surveillance systems is currently of interest due to its implications in the field of security. Surveillance human activities offers a context for the extraction of significant information such as scene motion, object classification, human identification, anomaly detection, as well as the analysis of interactions humans. A wide range of research possibilities are open in relation to visual surveillance and video tracking.
Tracking is a significant and difficult problem that arouses interest among computer vision researchers. The objective of tracking is to establish correspondence of objects and object parts between consecutive frames of video. It is a significant task in most of the surveillance applications. The conventional approach to object tracking is based on the difference between the current frame and the background Image.
In this work focuses on developing a framework to detect moving objects and generate reliable tracks from real-world surveillance video_2D. After setting up a basic system that can serve as platform for further automatic tracking research. It based on object motion in different parts of the scene. The proposed algorithm, consisting of five stages i.e. image Sequence (video), motion segmentation, noise removal, object tracking and alarm system.
Proposed algorithm can process in real time, a real time system is one logical correctness is based both on the correctness of the outputs and their timeliness, in this thesis the result of experiments get high performance for both real-time video and recorded video with different sizes the limited of this system is fixed blob size and background. It is dependent on the position of the camera near or far from the detected objects. Total execution time is small.