视觉原理指导下的动目标检测与跟踪新方法研究

Visual object tracking is a comprehensive technique for locating and tracking object by analyzing and understanding video information captured by visual sensor. It has an important status in the field of computer vision. In the research and application relating to visual object tracking, robustness is the most basic and important problem..Recently, among various visual tracking algorithms, particle filter tracking is arobust one that is able to solve the popular problems of un-linear object state andun-Gaussian noise distribution, track various object states simultaneously, and adapts to not only stationary visual platform but also moving visual platform such that it has drawn considerable attention both in the research and applications on visual object tracking. However, after in-depth study of particle filter tracking, it is found that particle filter algorithm, object feature model, and similarity measurement of feature model are the three key aspects of particle filter tracking, and they all have some problems to be addressed. Furthermore, like other visual object tracking algorithms,particle filter tracking also lacks of sufficient intelligence so that it cannot handle as human a variety of complex environment changing and choose a suitable scheme in real-time. Therefore, it is of great significance to deal with the problems lying in particle filter tracking and explore external mechanism of human vision to endow particle filter tracking with some intelligence in order to enhance the robustness of particle filter tracking..After the survey and analysis of the current research work, we present in this.thesis our research on the problems of visual object tracking based on particle filter.with the aim to promote the robustness of visual object tracking algorithms by.analyzing the three aspects - particle filter algorithm, model similarity measurement,and object feature model - of particle filter tracking that influence its robustness andinvestigating the external mechanism and physiological characteristics of human visual system so as to improve the intelligence of particle filter tracking. In this project,an adaptive control model for adjusting noise distribution, a modified version of coefficient for similarity measure between feature models, an object feature model named elliptical region covariance descriptor which enables fusion of various spatial-temporal features, a visual tracking framework that simulates human visual intelligence with the corresponding algorithms, and a computation model of the top-down visual attention mechanism are proposed.

针对动态背景下的目标可靠跟踪问题，基于人类视觉系统原理，在粒子滤波框架下，本项目提出一种新的动目标检测、目标多特征表示及运动预测机制。本项目利用高精度视频配准技术获得目标的运动信息，通过混合准则定义物体整体显著性；根据目标受背景干扰程度分别处理的策略，采取融合目标多特征的方法计算粒子的权值；将目标运动矢量引入粒子转移矩阵，建立动态运动模型的粒子状态转移机制，提高粒子的利用效率及跟踪速度；对于目标的遮挡，根据视觉跟踪全局性原理，通过滤波迭代预测目标位置，利用数据关联和航迹管理对目标进行可靠跟踪。本项目提出的粒子滤波与视觉原理结合的新方法及改进算法具有估计精度较高、鲁棒性较强的优势，可以满足全局跟踪要求，且具备自动恢复对目标继续跟踪的能力。本项目研究的成果，对解决无人驾驶设备的自动导航与制导能力，进一步将视觉原理应用于图像处理领域有积极的推动作用。

本课题主要针对视频监视技术中的目标可靠跟踪问题，在粒子滤波框架下，基于视觉特性，研究了视频增强预处理、动目标检测、目标多特征表示及运动预测机制。从全新角度推导出大气散射模型中各参数的求法，提出了一种基于面部法线的视点定位方法，深入研究了动态及静态显著图和人眼视网膜感受野模型，提出一种新的感兴趣目标提取方法。提出了机器学习的粒子滤波目标跟踪方法，以及一种单幅图像自动积测量方法，并研制了目标跟踪的原理样机。本项目研究的成果，对解决无人驾驶设备的自动导航与制导，进一步将视觉原理应用于图像处理领域有积极的推动作用。