基于粒子滤波的自适应目标跟踪算法研究
发布时间：2018-11-09 14:05
【摘要】：随着社会的信息化水平日益提高,传统产业开始利用信息技术来提高生产效率、减少人力消耗,而计算机视觉技术已经越来越多的被应用于民用领域和军事领域,生物特征识别、智能监控、无人驾驶、智能武器等新兴的概念开始不断升温。其中,视频目标跟踪技术是计算机视觉领域中的一个经典研究课题,但是由于实际场景中往往存在光照变化、运动状态突变、目标遮挡、相似物体干扰等复杂情况,当前已有的目标跟踪技术仍难以满足实际应用的需求。目标跟踪问题可以看作是由感兴趣目标先前得知的位置来预测其在后续视频序列中的空间位置,这是一个根据先验条件来对当前状态进行估计、验证的过程,因此可以利用贝叶斯状态估计的思想来对问题进行求解。本文正是对于其中最为经典的粒子滤波算法进行研究,探讨了视频目标跟踪中的一些关键性问题,主要的创新工作与研究成果包括以下几方面:1.针对传统粒子滤波目标跟踪方法中粒子的多样性不足以及易受场景干扰的问题,提出一种改进的免疫粒子滤波目标跟踪方法,该方法基于人工免疫算法的思想,根据目标跟踪中的关键性问题加入了抗体记忆库、粒子集可信度判定等过程,以提高算法在较复杂场景中的鲁棒性。2.建立合理的目标模型是粒子集更新结果趋向于目标状态真实值的重要前提,本文针对传统算法中的单一目标模型适应性较差的问题,提出了加入自适应学习机制的外观模型与运动模型,同时利用了特征分片、背景权重等思想,并且给出了相应的似然性计算方法。3.针对单目标粒子滤波跟踪方法直接应用到多目标跟踪问题时易出现的问题,提出了一个快速的交互目标判定与匹配算法,该方法适用于粒子滤波框架下的跟踪方法,可以在一定程度上提高多目标跟踪的准确性。本文尝试通过对传统的粒子滤波目标跟踪算法进行改进,使其在较为复杂的实际场景中提高性能。分别在Visual Tracker Benchmark测试库、PETS 2009Benchmark Data测试库以及车载相机拍摄的动态场景中选择了多段典型的视频进行算法的对比实验与分析,通过L1-偏差、目标区域覆盖比、多目标跟踪精确度、算法运行速度等统计指标验证了所提算法较传统方面具有明显的提高,在实际场景中达到了较好的适应性、鲁棒性和实时性。
[Abstract]:With the increasing level of information technology in society, traditional industries begin to use information technology to improve production efficiency and reduce human consumption, and computer vision technology has been more and more used in civilian and military fields. New concepts such as biometric identification, intelligent surveillance, driverless and intelligent weapons are starting to heat up. Among them, video target tracking technology is a classical research topic in the field of computer vision. However, because of the complex situation, such as illumination change, moving state mutation, object occlusion, similar object interference and so on, in the actual scene, video target tracking technology often exists in the field of computer vision. The existing target tracking technology is still difficult to meet the needs of practical applications. The target tracking problem can be regarded as predicting the spatial position of the object of interest in the subsequent video sequence from the position previously known, which is a process of estimating and verifying the current state according to a priori condition. Therefore, Bayesian state estimation can be used to solve the problem. In this paper, the most classical particle filter algorithm is studied, and some key problems in video target tracking are discussed. The main innovative work and research results include the following aspects: 1. Aiming at the shortage of particle diversity and the vulnerability to scene interference in traditional particle filter target tracking methods, an improved immune particle filter target tracking method is proposed, which is based on the idea of artificial immune algorithm. According to the key problems in target tracking, the antibody memory library and particle set reliability evaluation are added to improve the robustness of the algorithm in more complex scenarios. 2. Establishing a reasonable target model is an important prerequisite for updating the result of particle set towards the real value of target state. This paper aims at the problem of poor adaptability of single objective model in traditional algorithm. The appearance model and motion model with adaptive learning mechanism are put forward, and the idea of feature segmentation and background weight are used, and the corresponding likelihood calculation method is given. 3. Aiming at the problem that single target particle filter tracking method is easy to appear when it is directly applied to multi-target tracking problem, a fast interactive target determination and matching algorithm is proposed, which is suitable for tracking method under particle filter framework. The accuracy of multi-target tracking can be improved to some extent. This paper attempts to improve the traditional particle filter target tracking algorithm to improve its performance in more complex practical scenarios. In the dynamic scene of Visual Tracker Benchmark test library, PETS 2009Benchmark Data test library and vehicle camera, we choose several typical video to carry on the contrast experiment and analysis, through L1-deviation, coverage ratio of target area, precision of multi-target tracking. The algorithm running speed and other statistical indicators verify that the proposed algorithm has obvious improvement compared with the traditional algorithm, and achieves better adaptability, robustness and real-time performance in the actual scene.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
[Abstract]:With the increasing level of information technology in society, traditional industries begin to use information technology to improve production efficiency and reduce human consumption, and computer vision technology has been more and more used in civilian and military fields. New concepts such as biometric identification, intelligent surveillance, driverless and intelligent weapons are starting to heat up. Among them, video target tracking technology is a classical research topic in the field of computer vision. However, because of the complex situation, such as illumination change, moving state mutation, object occlusion, similar object interference and so on, in the actual scene, video target tracking technology often exists in the field of computer vision. The existing target tracking technology is still difficult to meet the needs of practical applications. The target tracking problem can be regarded as predicting the spatial position of the object of interest in the subsequent video sequence from the position previously known, which is a process of estimating and verifying the current state according to a priori condition. Therefore, Bayesian state estimation can be used to solve the problem. In this paper, the most classical particle filter algorithm is studied, and some key problems in video target tracking are discussed. The main innovative work and research results include the following aspects: 1. Aiming at the shortage of particle diversity and the vulnerability to scene interference in traditional particle filter target tracking methods, an improved immune particle filter target tracking method is proposed, which is based on the idea of artificial immune algorithm. According to the key problems in target tracking, the antibody memory library and particle set reliability evaluation are added to improve the robustness of the algorithm in more complex scenarios. 2. Establishing a reasonable target model is an important prerequisite for updating the result of particle set towards the real value of target state. This paper aims at the problem of poor adaptability of single objective model in traditional algorithm. The appearance model and motion model with adaptive learning mechanism are put forward, and the idea of feature segmentation and background weight are used, and the corresponding likelihood calculation method is given. 3. Aiming at the problem that single target particle filter tracking method is easy to appear when it is directly applied to multi-target tracking problem, a fast interactive target determination and matching algorithm is proposed, which is suitable for tracking method under particle filter framework. The accuracy of multi-target tracking can be improved to some extent. This paper attempts to improve the traditional particle filter target tracking algorithm to improve its performance in more complex practical scenarios. In the dynamic scene of Visual Tracker Benchmark test library, PETS 2009Benchmark Data test library and vehicle camera, we choose several typical video to carry on the contrast experiment and analysis, through L1-deviation, coverage ratio of target area, precision of multi-target tracking. The algorithm running speed and other statistical indicators verify that the proposed algorithm has obvious improvement compared with the traditional algorithm, and achieves better adaptability, robustness and real-time performance in the actual scene.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
【参考文献】
相关期刊论文 前9条
1 李文辉;陈昱昊;王莹;;自适应的免疫粒子滤波车辆跟踪算法[J];吉林大学学报(理学版);2016年05期
2 林涛;刘以安;;自适应蚁群算法在多目标跟踪中的应用[J];计算机仿真;2014年09期
3 肖沁雨;;智能视频监控关键技术分析[J];制造业自动化;2012年12期
4 蒋恋华;甘朝晖;蒋e，
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