基于Grassmann流形的粒子滤波多目标跟踪方法研究

项目名称： 基于Grassmann流形的粒子滤波多目标跟踪方法研究

项目编号： No.61503274

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 谢英红

作者单位： 天津大学

项目金额： 21万元

中文摘要： 复杂背景下建立准确可靠的表观特征模型是实现形变目标稳定跟踪的关键。现有的平直欧式向量空间已无法准确表达不同姿态下的形变目标表观特征。Grassmann流形不仅具有光滑曲面的空间表达方式，且具有更为适合形变目标表观特征的度量。本项目基于Grassmann流形，研究形变目标表观建模方法，利用状态空间的内蕴几何特性，设计估计表观特征的粒子滤波算法。研究仿射变换流形，建立目标的几何形变模型，设计预测几何形变的粒子滤波算法。结合两类滤波器，交替进行跟踪过程与在线学习过程，实现目标跟踪。研究基于Grassmann流形的背景遮挡处理策略，在目标表观特征空间在线学习过程中，屏蔽异常信息，保证特征空间的准确性。针对多目标互遮挡情况，研究流形上的角点检测与分类算法，正确区分目标重叠区域内各目标，特别是遮挡分裂后运动方向突然发生改变的目标，实现多目标稳定跟踪。最后，研究流形上的自适应粒子滤波算法，提高计算效率。

中文关键词： 目标跟踪；李群；Grassmann流形；表观模型；粒子滤波

英文摘要： In complex background, building accurate and reliable appearance feature model is the key to achieving stable tracking result for deformation object, while existing flat European vector space has been unable to describe the appearance feature of deformation object area under different posture conditions accurately. Grassmann manifold not only has the space expression of smooth curved surface, but also has appropriate metrics for deformable object. The project researches the method for building the appearance feature of deformation object based on Grassmann manifold, taking use of the intrinsic geometry characteristics of state space to design the particle filtering algorithm for predicting appearance feature, it also researches affine transformation manifold to establish the geometric deformation model, and design the particle filtering algorithm for estimating the object geometric deformation. Combining the two filters, alternately tracking process and online study process, it realizes object tracking. For the situation of object obscured by the background, the project researches on the processing strategy against occlusion based on Grassmann manifold. In the process of online study for object appearance feature space, abnormal information will be shielded, to guarantee the accuracy of the feature space. For mutual occlusion problem in multi-object tracking process, it researches the bilateral filtering based on Lie group, and establishes methods for corner detection and classification, designing the algorithm for separating the objects correctly in occlusion area, even can distinguish the objects changing its motion direction after occlusion to accomplish multi object tracking stably.Finally it researches the method for adaptive particle filtering on manifold to improve computational efficiency.

英文关键词： object tracking;Lie group;Grassmann manifold;appearance model ;particle filetering