基于二值特征描述符的目标表示及其应用

项目名称： 基于二值特征描述符的目标表示及其应用

项目编号： No.61472442

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： 杨源

作者单位： 中国人民解放军空军工程大学

项目金额： 81万元

中文摘要： 建立鲁棒高效的目标特征描述符，是实现目标检测、跟踪与识别等视觉任务系统的关键问题，目前是计算机视觉和模式识别领域的研究热点。与传统的实值特征描述符用实数向量表示目标相比，二值特征描述符用一串二值向量表示目标，通过汉明距离实现快速匹配，因此它具有快速、低存储的特点，是特征描述符的发展趋势。本项目将研究基于二值编码的特征描述符，寻找保持目标内部结构信息的哈希变换，将目标从欧式空间投影到汉明空间，降低特征描述符的存储空间，结合不同视觉任务系统的先验知识和约束条件，学习与目标语义相关的距离度量，研究汉明空间的快速最近邻搜索策略，实现实时匹配，并将其应用于目标检测和目标跟踪中，为实际应用奠定良好的基础。因此，本项目的开展和预期成果将为高效的目标表示模型提供新思路和新方法，是一项既有理论意义又有广阔应用前景的课题。

中文关键词： 目标表示；二值特征描述符；哈希学习；目标检测；目标跟踪

英文摘要： The robust and fast feature descriptor is the key problem for the object detection,tracking and recognition, which is the hot topic in the field of computer vision and pattern recognition. The object is be represented by the real number vector in the classic real-value feature descriptor. The binary feature descriptor explores the binary vector to descriptor the object, which can matched fast by the hamming distance. The binary feature descriptor is real-time and low memory, which is the development trend of the feature descriptor.The feature descriptor based on binary coding will be researched in this project. The hashing projection is learned by keeping the object structure information in the Euclidean space. The object is mapped to the hamming space and the memory is decreased for the feature descriptor. The distance metric is learned by the machine learning and fast approximate nearest neighbor searching is be researched. The binary feature descriptor is used to object detection and tracking, which will play an important role in the real system. Therefore, the implementation and the expected results of the project will provide new ideas and new methods for the effective and efficient object representation model, which has a broad theoretical basis for the application of prospect research.

英文关键词： object representation;binary feature descriptor;hash learning;object detection;object tracking