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移动摄像机下目标的轮廓与区域跟踪
其他题名Contour and Region Tracking Under Mobile Camera
李威
学位类型工学博士
导师胡卫明
2012-06-03
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业计算机应用技术
关键词目标的轮廓跟踪 目标的区域跟踪 表观模型 Contour Based Object Tracking Region Based Object Tracking Observation Model
摘要本文的工作以移动摄像机下目标轮廓与区域跟踪为目标,分别对以下四个问题进行了深入的探讨和分析:(1)快速运动物体的轮廓跟踪;(2)如何有效的提高轮廓跟踪的精度;(3)区域跟踪中的表观建模大量的实验表明我们方法的有效性和鲁棒性。论文的主要从以下三个方面为主体开展了研究: 1. 针对轮廓跟踪中遇到的快速运动和跟踪精度两个问题,分别提出了相应的解决思路。 为了能跟踪快速运动的物体的轮廓,我们提出了基于粒子群优化的轮廓跟踪框架,该框架有效的解决了快速运动对轮廓跟踪造成的挑战。同时我们提出了基于Adaboosting判别模型的轮廓跟踪算法,我们把Adaboosting判别模型融入到轮廓进化的能量函数中,提高了轮廓跟踪的精度以及处理嘈杂背景的能力。 2. 针对区域跟踪的表观建模问题,我们首先提出了基于概率索引直方图的目标表观模型,该模型能够有效的解决传统的直方图表示物体的局限性,同时引入了空间距离和比值不相似度距离来提高直方图度量的鲁棒性。 其次我们探讨了不同类型的表观模型的融合问题,提出了基于Volterra核嵌入判别的表观模型,并在图嵌入框架下融合产生式模型,来使得我们模型具备产生式和判别式模型的优点,能得到更加鲁棒的跟踪结果。 最后我们探讨了表观建模的多特征融合问题,提出了基于多任务联合稀疏表示的跟踪方法,通过多任务联合优化有效的解决了稀疏表示中多特征融合的问题。 3. 在论文的最后一部分,我们开展了针对手的跟踪算法的研究,因为手在目标跟踪的特定应用-人机交互中起了重要作用。 我们提出了基于RCD多特征融合准则的手跟踪框架。相比于其他经典的通用目标跟踪算法,我们的算法在手跟踪的任务上,能够取得更鲁棒的结果。
其他摘要This thesis focuses on robust contour and region tracking under mobile camera. Specifically, we mainly discuss the following four sub-topics: (1) contour tracking with abrupt motion; (2) improving the accuracy of contour tracking; (3) the construction of observation model for region tracking. The main components of our thesis are listed as follows: 1. We firstly address the problem of abrupt motion and accuracy in the contour tracking area. In order to track the contour of object with abrupt motion, we propose a particle swarm optimization based contour tracking framework. In this framework, the problem imposed by the abrupt motion is efficiently solved. Meanwhile, in order to improve the accuracy of contour tracking, we propose a contour tracking algorithm that based on the discriminative model. The Adaboosting algorithm based discriminative model is adopted in constructing the energy function for the contour evolution. Our algorithm improves the accuracy of contour tracking and its ability in handling noisy environment. 2. In the second part of our thesis, we mainly concentrate on constructing a robust appearance model that can provide more robust region tracking results. Firstly, we propose a probabilistic index histogram for the appearance of the target. Our model can efficiently overcome some limitations of the histogram representation. We also introduce spatial distance and bin-ratio dissimilarity for more robust histogram comparison. Secondly, we explore the problem of fusing different types of appearance model. We propose a Volterra embedding based discriminative model for the appearance of the target. Furthermore, we compensate the discriminative model with the generative object model through a unified graph embedding framework. So our model has the complemental advantage of both models and can provide more robust tracking results. At last, we explore the problem of multi-cue fusion in constructing the appearance model. We propose a multi-task joint sparse representation framework for robust object tracking. The problem of multiple features fusion in the sparse representation is efficiently handled through the joint optimization of multi-task. 3. In the last part of our thesis, we focus on design a robust tracking algorithm for the hand. This is because hand is one of the most important tools in the human-computer interaction, it is meaningful to design a particular algorithm for it. We propose a RCD criterion based multi-cue fusion ...
馆藏号XWLW1816
其他标识符200918014629087
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6476
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
李威. 移动摄像机下目标的轮廓与区域跟踪[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
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