基于粒子重叠分布及实时稀疏字典的行人跟踪方法研究

项目名称： 基于粒子重叠分布及实时稀疏字典的行人跟踪方法研究

项目编号： No.61203269

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 杨阳

作者单位： 山东大学

项目金额： 24万元

中文摘要： 本课题以复杂场景下行人目标跟踪问题为背景，以粒子滤波的重叠分布特性为切入点，针对粒子滤波中特征计算及状态估计两个核心环节，探索实时性和鲁棒性相结合的行人跟踪方法。针对计算复杂度过高这一目前限制粒子滤波算法实时应用的瓶颈，拟基于粒子滤波过程中粒子的重叠分布特性及特征可加性原理，研究多尺度自适应分区域的粒子特征值的计算方法，预期可大大简化重叠粒子的特征提取；在粒子状态的鲁棒估计方面，基于新兴的压缩感知理论，从时变稀疏字典的建立机理入手，通过分析遮挡、形变等干扰因素对行人目标特征的影响，提出一种行人目标描述字典的实时生成方法。在此基础上，对建立的动态字典进行基于压缩感知理论的l1范数求解，以稀疏度作为粒子加权值，将有效实现目标大小变化及存在遮挡时的鲁棒跟踪。本课题对粒子滤波过程中重叠粒子分布机理、有限可加特征的选取原则、应用方法以及在线稀疏字典的高效自动生成方法等关键问题展开研究。

中文关键词： 粒子滤波；压缩感知；行人跟踪；鲁棒；稀疏字典

英文摘要： This project focuses on pedestrian tracking in complex scenes based on particle filtering method. Considering the intense overlapping of the particles, the two key steps of particle filtering, namely feature extraction and state estimation, are studied to achieve both real-time and robust tracking. To address the high computational complexity problem in feature extraction step, an adaptive multi-scale sub-regional method is proposed based on the overlapping distribution and the feature additive rule, which can effectively release the computation burden of the feature extraction step. The compressed sensing theory is introduced to achieve robust estimation of the particle state. In order to establish the time-varying pedestrian sparse dictionary, the occlusion, deformation and other factors to the pedestrians target characteristics are considered. From the above analysis, the effectiveness of sparse expression based on l1 norm optimization, sparsity based weighting of the particles will all contribute to achieve robust tracking in complex scenes regardless of the difficulties introduced by varying target size, illumination conditions as well as the occlusion. The key issues of (1) the mechanism of overlapping distribution of particles, (2) the theoretical principle of the feature additive rule and practical featu

英文关键词： Particle Filter；Compressed Sensing；Pedestrian Tracking；Robust；Sparse Dictionary