基于多任务稀疏学习的视频行为理解

项目名称： 基于多任务稀疏学习的视频行为理解

项目编号： No.61472420

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： 原春锋

作者单位： 中国科学院自动化研究所

项目金额： 81万元

中文摘要： 现有的视频行为理解主要集中在行为分类和识别上，对于行为检测特别是预测的研究相对较少。本项目将视频行为理解的研究从单纯的行为识别扩展到对行为进行识别的同时实现检测和预测；从分割好的仅包含一种行为的短视频提升到包含多人多种行为的复杂长视频的研究；从离线的分类处理到在线的检测预测处理；从理论研究为主要目标发展到越来越关注在实际中的应用价值。主要研究内容包括：（1）底层特征提取，拟提出基于有向运动显著性区域的描述子和基于图的热核结构描述子；（2）中层视频行为表示，拟提出一种基于非参数贝叶斯模型的多任务稀疏学习方法进行多特征联合视频表示；（3）高层行为检测、预测，拟提出一种基于随机森林和Hough投票的检测策略，和一种基于结构化输出支持向量机的行为预测框架。本项目立足于前沿，将在行为理解领域做出一系列国际领先工作，并为视频检索、视觉智能监控等领域提供相关理论和关键技术。

中文关键词： 视频分析；稀疏表示；特征提取；计算机视觉

英文摘要： Current human action understanding mainly focuses on action classification and recognition. There is less research on action detection and especially on action prediction. This project reforms the study of human action understanding from simple recognition to detection and prediction, from handling a segmented short video containing only one action class to the complex long video containing multiple persons and multiple action classes, from offline classification processing to online detection and prediction, and from theoretical research as the main objective to paying more attention on the practical application value. The main research content includes: (1) for low-level feature extraction, propose a oriented motion salient region descriptor and a graph based heat kernel structural descriptor; (2) for middle-level video action representation, propose a non-parametric Bayesian based multi-task sparse learning model for multiple features joint representation; (3) for high-level action detection and prediction, develop a random forest and hough voting based action detection strategy and a structured output SVM (SOSVM) based action prediction framework. The project stands on the academic frontier and will make a series of advanced work on human action understanding to provide the related theories and key techniques for video retrieval, visual intelligent surveillance and other areas.

英文关键词： video analysis;sparse representation;feature extraction;computer vision