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Development of Mobile Image Processing Platform

Author: LiYunPeng
Tutor: ZhangDongQuan
School: Beijing Jiaotong University
Course: Safety Technology and Engineering
Keywords: Modular design Image preprocessing Target detection and tracking Camshift Particle filter
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 64
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Abstract


In recent years, the technology of moving object tracking based on visual image is a hot research topic in the field of computer vision. Thus, object tracking using visual image has been widely used in security surveillance, navigation and guidance. It is difficult to track object accurately and spontaneously in complex situations. Hence, the considered research has the important theory and application value. The modular design is suitable for the production of modern industrial workenvironment and task variability. It can quickly adapt to task changes in demand, improving work efficiency greatly and reducing costs. This thesis is based on a modular design approach, studying the configuration of the mobile image processing platforms, control and the procedure.Firstly, the current research and development of object tracking is introduced, and the existing object detection and object tracking methods are analyzed, and the technology difficulties of object tracking are given.Secondly, the modular design approach is studied. Research and analysis for the mobile platform of the subject are based on modular thinking. The mechanical part and the control system of the platform are designed using modular techniques.Subsequently, each step of target recognition and tracking including image preprocessing, moving target recognition and tracking is studied. A modified optimization method is proposed according to the specific application environment. An optimization method to reduce the computational complexity is proposed in the image noise cancellation. Both Frame difference method and the optical flow method are optimized. Camshift Tracker, Kalman filter, particle filter tracking algorithm are studied in depth in the moving target tracking. Camshift algorithm is combined with Kalman filter for moving object, by improving the search strategy to achieve the stability of fast tracking a moving object, and the satisfying results are obtained. The moving object tracking algorithm which is integrated with visual images and particle filter is studied. When the object is occluded and exist various types of noise, the template update algorithm is proposed to improve the robustness of algorithm. However, in order to overcome the problem of the degradation phenomena and the computational cost of the particle filter, the Meanshift algorithm is combined with the particle filter algorithm, the results show that the tracking algorithm can maintain real-time and robustness under the condition of fast moving, occlusion, deformation and various types of noise.Finally a succinct multi-modules system is established using some functions and the basic framework of object tracking of OpenCV. The system can do automatic motion detection and tracking with high real-time performance and moderate robustness.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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