Tracking can be defined as the creation of temporal correspondence among detected objects from frame to frame. After identifying moving object in a given scene, the next step in video analysis is tracking. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. First, it must be robust against changes in illumination Second, it should avoid detecting non- stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. There are many challenges in developing a good object detection algorithm. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. The making of video surveillance system best requires fast, reliable and robust algorithm for moving object detection and tracking system. Image sequence of moving target tracking, is detected in eachįrame of the goal of the various independence movements, or the Users interested in the movement areas (such as the human body, vehicles, etc.), and extract the object's position information, to receive the various objectives Trajectory. Our paper is based on image processing to achieve the objectives of detection and tracking system. ![]() The current detection and tracking system is mainly using two techniques: First, the use of radar technology for tracking while another is based on image processing technology to achieve target tracking. It has important research value, which attracting more and more researchers at home and abroad. ![]() Moving object detection and tracking in the military, intelligence monitoring, human machine interface, virtual reality, motion analysis and many other fields have Wide application prospects in science and engineering. Keywords: Video Surveillance, Moving Object Detection, Background Subtraction, Moving Object Tracking, Frame differencing, Mixture of Gaussian modelĪs surveillance systems are becoming more popular, robust detection and tracking techniques are needed to determine moving objects. Thus, making it faster and more suitable for real time surveillance applications, This study used IFD(Inter- Frame Differencing algorithm) and bounding box method to track the objects. The proposed technique combines simple frame difference (FD), simple adaptive background subtraction (BS), and accurate Gaussian modeling to benefit from the high detection accuracy of Mixture of Gaussian solution (MoG) in outdoor scenes while reducing the computations. The proposed system is capable of adapting to dynamic scene, removing shadow, and distinguishing left/removed objects both in indoor and outdoor. ![]() This paper presents detection and tracking system of moving objects based on matlab.It is described for segmenting moving objects from the scene. It can handle object detection in indoor or outdoor environment and under changing illumination conditions. The system can process both color and gray images from a stationary camera. The video surveillance system requires fast, reliable and robust algorithms for moving object detection and tracking. This thesis is committed to the problems of defining and developing the basic building blocks of video surveillance system. In this thesis, video surveillance system with moving object detection and tracking capabilities is presented. ![]() Video surveillance has been in used in the monitor security sensitive areas (such as banks, department stores, highways, crowded public places and borders, and etc.). Detection and Tracking System of Moving Objects Based on MATLABĮlectrical and Electronics Technology Department Federal TVET InstituteĪbstract: Moving Object detection and tracking are receiving a growing attention with the emergence of surveillance systems.
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