【作者】 肖杨；

【导师】 王保保；

【作者基本信息】 西安电子科技大学 ， 计算机应用技术， 2010， 硕士

【摘要】 工业缝纫机零部件的运动轨迹数据可用于数据化对比分析实际行为与设计行为之间的差异,指导缝纫机产品的研发,但该数据难以通过人工观察和记录的方式获取。图像处理方法为此提供了一种新的辅助方式,通过跟踪缝纫机视频图像中的零部件获得运动轨迹数据。目标跟踪是这一过程的关键因素,对分析问题、得出正确结论起着决定性的作用,因此对适用于工业缝纫机运动图像的目标跟踪方法的研究具有重要的应用价值。本文以高速照相机所拍摄的工业缝纫机运动图像序列为研究对象,设计并实现了高速缝纫机影像跟踪系统,解决了原有系统中不同运动方式下跟踪稳定性不良的问题,实现了缝纫机零部件稳定跟踪的功能。首先,介绍了相关跟踪算法的基本理论。基于对图像序列特点的分析、用户需求的考虑和四种用于灰度图像的相关跟踪算法的仿真比较,提出使用平均平方差相关跟踪算法来完成对零部件的跟踪,实现了短期稳定跟踪。然后,在介绍粒子滤波理论的基础上,将相关跟踪算法嵌入粒子滤波跟踪框架中,采用粒子传播和观测来改进传统相关算法的匹配搜索过程,实现了缝纫机零部件的长期稳定跟踪。最后,引入重采样平滑机制,并改进平滑方式,能够在繁殖大权值粒子的基础上提高粒子的多样性,加强跟踪的稳定性。并经过大量实验确定粒子总数,保持跟踪精度的同时有效减少了跟踪所用时间。实验结果表明,按上述方法,零部件跟踪的稳定性大大提高,并获得精度高于原系统的零部件运动轨迹,为工业缝纫机的设计研究提供了可靠的分析数据。更多还原

【Abstract】 The trajectory data of industrial sewing machines are usually used to give a data-based comparative analysis of the differences between actual moving behavior and designed moving behavior, and provide a guide to the research and development work of the sewing product, but the data are difficult to observe and record by artificial means. Image processing method provides a new way for it:obtain the trajectory data by tracking spare parts in the sewing video images. Target tracking is a key factor in this process, and plays a decisive role in analyzing problems and drawing the right conclusion, so the study of target tracking method applicable to high-speed sewing machine motion images has important application value.In this paper, with the motion image sequence of industrial sewing machines captured by high-speed camera as the research object, High-speed Sewing Machine Image Tracking System is designed and implemented, the problem of the poor tracking stability under different movement pattern in the original system is solved, and a steady tracking is achieved for sewing machine spare parts. First of all, the basic theory of correlation tracking algorithm is introduced. In view of the analysis of the image sequence’ characteristics, the consideration for user requirements and the comparison and simulation of four kinds of gray-scale image correlation tracking algorithms, the average squared difference correlation tracking algorithm is used to complete the tracking of spare parts and achieves a short-term stabilization tracking. Then, based on the introduction of the particle filter theory, embed the correlation tracking algorithm into the tracking framework of particle filter, use particle propagation and observation to replace the matching and searching process of the traditional correlation algorithm, and achieve long-term stability of the sewing machine spare parts tracking. Last, the method that it smoothes particles after random sampling is introduced for re-sampling and the smoothing way are improved. This re-sampling way improves the diversity of particles and enhances the stability of tracking with breeding power values particles. And after a large number of experiments the total number of particles is determined, effectively reducing the tracking time on the basis of maintaining tracking accuracy.Experimental results show that with the above method, the stability of tracking spare parts is greatly improved. More accurate trajectories of components are obtained than that of the original system and reliable analytical data are provided for the study of the sewing machine designing.更多还原