基于视觉注意和稀疏表示的行人检测与跟踪方法研究

项目名称： 基于视觉注意和稀疏表示的行人检测与跟踪方法研究

项目编号： No.61305042

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 张苗辉

作者单位： 河南大学

项目金额： 23万元

中文摘要： 基于视觉系统的行人检测与跟踪作为一种关键技术，是计算机视觉更高层次行为理解与分析的基础，它的检测精度和跟踪的鲁棒性直接影响到后续的各种诸如人机交互、行人行为的描述与理解等高级应用处理，但现实中的复杂场景以及人体姿态空间的高维和非线性等问题，使得设计稳健的行人检测与跟踪算法变得非常重要。本项目研究基于视觉注意机制的行人检测算法和基于稀疏表示的行人跟踪算法，同时研究多视觉特征自适应融合的跟踪策略。针对基于视觉系统的行人检测与跟踪中的技术难点，提出有效解决方案:(1)提出基于视觉注意机制的能有效描述人体轮廓的行人特征，通过对如颜色、梯度等底层特征进行映射转换, 提出新的更能反映人体本质属性的行人特征，通过对检测分类器进行优化组合，设计构建兼具分类精度和速度的行人检测分类器。(2)提出基于稀疏表示和特征块跟踪的滤波框架，提出有效的模板更新策略。(3)提出粒子群优化基于的多特征自适应融合的跟踪策略。

中文关键词： 行人检测与跟踪；视觉注意机制；稀疏表示；粒子群优化；多特征自适应融合

英文摘要： As a key technology, vision system based pedestrian detection and tracking is the basis of the higher level behavior analysis and understanding in the field of computer vision, its detection accuracy and tracking robustness will directly affect the subsequent varieties of advanced applications such as human-computer interaction, pedestrian behavioral descriptions and understanding etc. Because of the complexity in the actual scene and high dimension and nonlinear problem of the body posture space, it is much necessary for designing robust pedestrian detection and tracking algorithms. Therefore, the main focus of the study is as follows, visual attention mechanism based pedestrian detection algorithm, sparse representation based pedestrian tracking algorithm, multi-cue adaptive fusion tracking strategy. For the technical difficulties of vision based pedestrian detection and tracking, the effective solutions are proposed as follows: (1) Visual attention mechanism based pedestrian feature is proposed, which can efficiently describe body contours, another pedestrian feature, which can better reflect the nature property of human body, is simultaneously explored by mapping conversion of the underlying features such as color feature and gradient feature. In order to improve classification accuracy and real-time, acco

英文关键词： Pedestrian detection and tracking；Visual attention mechanism；Sparse representation；Particle swarm optimization；Adaptive multi-features fusion