Detection And Tracking Of Moving Object In Image Sequence Based On OpenCV

Detection and tracking of moving object in image sequence is one important area in the domain of digital image processing and computer vision. It's applications include fields like robot navigation, vision-based supervision, ecurity surveillance, medical image analysis, industrial automation, video processing, etc...Detection and tracking of moving object in image sequence is also the most complicated direction of computer vision and digital image processing techniques. It is a ample project that including video collection, digital image processing and application development.It's too complex for a engineer to accomplish all these works, at the same time the stability, practicability and general availability is difficult to guaranteed.OpenCV(Open Source Computer Vision Library) is a library for digital image processing and computer vision. It is developed by the visual interactivity group in Intel's microprocessor research Lab with C++ language. It can be used in window system and Linux system. The library is open source and can be free download from Intel's Web site. OpenCV provides various functions to extract frame from image sequence and video source ( such as: bitmap images, video files and real-time video camera ) and many of the standard image processing algorithms, these functions can be directly used in the video program for specific development projects.Aim at detecting, tracking and marking multipule specific targets in complex background. We use the basic framework of object tracking of OpenCV to establish a multi-modules system. The system is composed of Human-computer interaction interface module, foreground of moving objects detection module, blocks feature of moving objects detection module, blocks tracking module and track analysis module.We use this system doing a lot of experiment under various contexts, and tht experiments results are well wxamined. The results demonstrate that the system can do automatic motion detection and tracking with high real-time performance and moderate robustness. Since we develop this system under windows platform, how to migrate to other systems or embedded platform and further improve the versatility and robustness has become the focus of future research work.