多视角多姿态人体目标检测研究

项目名称： 多视角多姿态人体目标检测研究

项目编号： No.61271433

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 叶齐祥

作者单位： 中国科学院大学

项目金额： 76万元

中文摘要： 图像/视频中的目标检测是计算机视觉与图像理解领域的核心研究课题之一。而现有检测方法大多受到目标视角、形变、复杂背景等因素的困扰。本项目以多视角多姿态的人体目标为切入点，以拓扑优先认知理论与压缩感知理论为依据，研究目标的拓扑描述和分段线性稀疏表示方法；采用动态规划与逆最优化数学工具，求解拓扑形变及拓扑迁移模型；采用分段线性稀疏表示与形变模型相结合的方法解决多视角多姿态难题。尝试在计算机视觉中呈现拓扑优先的生物认知机理，进而实现高性能的目标检测算法。项目研究内容涉及了目标特征表示理论、分类器设计方法及形变问题。研究成果对于广义目标检测具有重要理论意义，对于视频监控、图像/视频检索、辅助安全驾驶等系统具有应用价值。

中文关键词： 目标检测；多姿态目标；人体检测；；

英文摘要： Object detection in image and video frames is one of the most important topics in computer vision and image understanding, while most of the existing object detection methods are challenged by object views, deformation and complex background. In this project, focusing on multi-view and multi-posture human objects, depending on the "global-first" visual cognition and compressed sensing theories, we investigate the topology description and piecewise linear sparse representation. We plan to use dynamic programming (DP) and inverse optimization methods to solve the topology deformation and transferring models. We also attempt to solve the multi-view and multi-posture problem by the fusion of piecewise linear sparse representation and deformable models. The research will realize the "global-first" visual cognition in computer vision and finally bring out a high performance object detection algorithm. The research content covers the object representation theory, classifier design method and object deformation problem. The research results are significant to general object detection theory and are valuable to practical applications such as intelligent video surveillance, content-based video retrieval and driver-assistant systems.

英文关键词： object detection；multi-posture object；human detection；；