面向复杂图像序列的光流运动估算模型及其在安全监控中的应用

项目名称： 面向复杂图像序列的光流运动估算模型及其在安全监控中的应用

项目编号： No.61472157

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 牛砚

作者单位： 吉林大学

项目金额： 68万元

中文摘要： 光流技术，作为视频运动估算的主要工具，已被成功应用于火星探测、无人驾驶、生物图像配准、视频压缩等领域。尽管在视频安全监控课题中被广泛使用，现有的光流技术对监控视频的高噪声、光照突变、人物运动、变形及运动遮挡等因素敏感，计算精确度尚有待提高。本课题首先在动作识别、姿态估计、行人检测任务框架中定量分析主流光流技术，明确安全监控对光流计算的实际要求。基于此，设计抗干扰的光流模型，包括：1）结合Pb边界检测算法，通过快速判别空间与时-空梯度张量矩阵秩数的关系，提取图像序列的遮挡及纹理信息，并将其嵌入光流模型，清晰还原运动边界；2）运用微分几何的Frenet标架及旋转不变量理论，建立适用于估算光照变化下、快速铰接式运动及仿射形变的光流模型；3）研究光流可靠性度量方法，构造新型非对称非局部光滑约束方程，用可靠光流向量约束不稳定光流向量，获得鲁棒的光流场。研究结果有望为监控系统智能化提供关键技术支撑。

中文关键词： 计算机视觉；视频分析；特征提取；图像配准；视频处理

英文摘要： Optical flow, as one of the main tools for motion estimation, has been successfully applied to Mars exploration, unmanned aerial vehicle, bio-image registration, video compression etc. Although optical flow techniques have been widely employed in video surveillance, they are challenged by factors such as noise, lighting changes, human motion, deformation and occlusion. Unfortunately, these factors are frequent in real-world video. Thus optical flow computation can only provide coarse information for video surveillance tasks. In this project, we first quantitatively analyze the effectiveness of state-of-the-art optical flow techniques in action recognition, pose estimation and pedestrian detection tasks, to learn the practical principles on modeling flow computation for video surveillance. Based on these principles, we investigate robust flow recovery by strategies as follows. 1) We investigate a fast method that compares the rank of the spatial structure tensor matrix and spatial-temporal structure tensor matrix. By incorporating it into Pb boundary detection, we detect the presence of motion boundaries and key points. This information is further embedded into the flow computation model, to preserve motion boundaries sharply in the flow field recovered. 2) We utilize Frenet frame and rotational invariants to construct a flow model that accurately estimates articulated motion under deformation and lighting changes. 3) We investigate new confidence measures for flow vectors, based on which, we design an asymmetric non-local optical flow smoothness constraint to regulate error-prone flow vectors by reliable flow vectors. This research is promising to achieve accurate and robust motion estimation for smart video applications.

英文关键词： Computer Vision;Video Analysis;Feature Extraction;Image Registration;Video Processing