基于机器学习的目标跟踪系统

发布时间：2018-03-26 21:15

本文选题：目标跟踪　切入点：机器学习　出处：《宁夏大学》2017年硕士论文

【摘要】：目标跟踪技术是计算机视觉领域重要技术之一,在不同领域都起到一定作用。目标跟踪技术的不断进步,也在不断提升人们的生活质量。一方面,传统的目标跟踪算法,在指定的场景、目标下往往能起到比较显著的效果,不论是时效性还是准确率,都有一定的优势,从长时间跟踪的层面看,传统目标跟踪算法的误差不断累积。另一方面,传统的目标跟踪算法在场景和目标上具有局限性,无法应用到其他目标或场景中。随着计算机的不断智能化,大数据时代的到来,无疑给机器学习带来了新的发展机遇。机器学习属于多领域交叉学科,从大量数据中学习产生新知识,并对未知事物进行预测。机器学习理论在目标跟踪上的应用,通过将目标跟踪问题转化为数据分类问题,简化了目标跟踪的复杂性,适用于各类复杂的目标跟踪问题。本文对目标跟踪算法进行研究,主要研究TLD算法,发现其在长时间单一目标跟踪问题上具有较高的准确率,但在运行速度上无法达到实时跟踪的效果。本文在TLD算法的框架基础上,提出了基于帧间差分法的TLD跟踪算法,经过大量实验数据表明,其在跟踪速度上较原算法提高了 3.9倍,因而本文将该算法应用到目标跟踪系统,并通过目标检测模块实现非监督的基于机器学习的目标跟踪系统。本文开展的具体研究工作如下:1、介绍了目标跟踪的发展历史、研究现状。介绍了帧间差分法、STC算法和TLD算法的原理,从理论上分析三者的优劣势;2、对比传统目标跟踪算法与基于机器学习的目标跟踪算法,仿真实现帧间差分法、STC算法、TLD算法在目标跟踪上的应用,并对结果进行对比分析;3、对TLD算法进行深入研究,分析各模块的运行机制,提出了帧间差分法的TLD跟踪算法,应用到目标跟踪系统,并进行大量实验,展示系统在视频图像跟踪的准确性;4、设计并完成了基于机器学习的目标跟踪系统,将本文所提出算法应用到目标跟踪系统中,实现其应用价值,并将系统移植到Linux系统,最后在此平台上进行实验,验证其跟踪效果。
[Abstract]:Target tracking technology is one of the important technologies in computer vision field, which plays a certain role in different fields. With the development of target tracking technology, people's quality of life is also being improved. On the one hand, traditional target tracking algorithm, In the specified scene, the target can often play a more significant effect, whether it is timeliness or accuracy, have certain advantages, from the long-term tracking level, the traditional target tracking algorithm error accumulation, on the other hand, The traditional target tracking algorithm is limited in scene and target, and can not be applied to other target or scene. There is no doubt that machine learning brings new opportunities for development. Machine learning belongs to a multi-domain interdisciplinary subject, learning from a large number of data to produce new knowledge, and to predict the unknown. The application of machine learning theory in target tracking. By transforming the target tracking problem into a data classification problem, it simplifies the complexity of target tracking and is suitable for all kinds of complex target tracking problems. In this paper, the target tracking algorithm is studied, and the TLD algorithm is mainly studied. It is found that it has high accuracy in long time single target tracking problem, but it can not achieve the effect of real-time tracking in running speed. In this paper, based on the frame of TLD algorithm, a TLD tracking algorithm based on inter-frame difference method is proposed. A large number of experimental data show that the tracking speed of the algorithm is 3.9 times higher than that of the original algorithm, so the algorithm is applied to the target tracking system in this paper. An unsupervised target tracking system based on machine learning is implemented through target detection module. The specific research work in this paper is as follows: 1. The development history of target tracking is introduced. This paper introduces the principle of inter-frame difference algorithm and TLD algorithm, analyzes their merits and demerits in theory, and compares the traditional target tracking algorithm with that based on machine learning. The application of TLD algorithm in target tracking is realized by simulation, and the results are compared and analyzed. The TLD algorithm is studied deeply, and the running mechanism of each module is analyzed, and the TLD tracking algorithm of inter-frame difference method is proposed. It is applied to the target tracking system, and a large number of experiments are carried out to show the accuracy of the system in video image tracking. A target tracking system based on machine learning is designed and completed. The algorithm proposed in this paper is applied to the target tracking system. The application value is realized, and the system is transplanted to Linux system. Finally, experiments are carried out on this platform to verify its tracking effect.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP181

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