子空间分析和稀疏表示的运动车辆鲁棒在线视频检测与辨识

项目名称： 子空间分析和稀疏表示的运动车辆鲁棒在线视频检测与辨识

项目编号： No.61304205

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： 孙伟

作者单位： 南京信息工程大学

项目金额： 24万元

中文摘要： 运动车辆的视频检测和辨识是智能交通的研究热点。运动目标的视频检测和辨识已成为众多应用的核心支撑技术。本项目针对复杂交通环境中运动车辆视频监控的需要，对运动车辆视频检测和辨识展开理论和方法研究。本项目拟在研究格拉斯曼流形的随机梯度下降最优化模型及交替方向乘子非凸优化算法的基础上，从对偶角度实现鲁棒性子空间的辨识与跟踪，进而从视频中分离出前景中的运动车辆。同时从图像的全局轮廓和局部细节出发，利用主成分分析约简图像字典矩阵，基于贝叶斯先验知识优化稀疏最优解，对车辆图像进行稀疏表示和重构，实现车辆的在线融合辨识。最后，在真实交通环境下搭建车辆视频检测与辨识实验平台，验证和改进所提算法的性能。本项目从格拉斯曼流形的随机梯度下降最优化模型入手，从子空间辨识与跟踪的角度阐明运动目标和背景分离的本质问题，从图像的稀疏表示和重构角度提出运动车辆在线辨识的实现方法，为智能交通视频监控提供新思路和理论依据。

中文关键词： 车型识别；Gabor特征；SIFT算子；k近邻分类器；稀疏表示分类器

英文摘要： Video detection and identification of moving vehicle is a hot research topic in intelligent transportation. The video detection and identification of moving target has become the key and supportive technology for many applications. Aiming at the requirements for video monitoring of moving vehicle in complex transportation environment, this project conducts theoretical and method research on video detection and identification of moving vehicle. This project is proposed to study on identification and tracking of robust subspace from the angle of dual, and to relize the separation of moving vehicle from video, which is based on the stochastic gradient descent optimization framework of grassmannian manifold and non-convex optimization algorithm of alternating direction multiplier. Meanwhile, the project studies on sparse representation and reconstruction for vehicle image to realize the online fusion identification of vehicle, considering global outline and local details of the vehicle image, by simplifying image dictionary matrix using principal component analysis and optimizing sparse solution based on prior knowledge of bayesian theory. Finally, the project constructs the experimental platform for the video detection and identification of moving vehicle in real transportation environment, further verifies and imp

英文关键词： vehicle type recognition；Gabor feature；SIFT operator；k-nearest neighbor classifier；sparse-representation based classifier