Nonseparable Multiclass Learning for Object Tracking

机器人和计算机视觉项目摘要提案号：0328802题目：用于对象跟踪的不可分离多类学习PI：沈晓彤俄亥俄州立大学研究所博士对象跟踪是图像和视频处理的一项重要技术，在许多应用中，包括视觉引导自动化、自动目标识别、基于对象的视频压缩和人脸识别。 作为对象跟踪技术基础的一个基本工具是机器分类。二进制PSI学习的最新发展使我们能够进一步实现不可分离和多类情况下更高的泛化精度。该提案提出了一个跨学科的研究计划，以解决通过这种新的学习工具的多目标跟踪的问题。 该项目将研究如何通过研究其泛化能力以及评估方法来最大限度地提高PSI学习的准确性。 具体的预期成果是：（a）建立一个稳定的基础，PSI学习和学习理论，优化理论和算法的进一步发展，（B）具体开发的机制，对象跟踪和提取的多媒体压缩。拟议的项目预计将产生广泛的影响，教育，研究，经济和社会在整个。特别是，该项目开发的技术广泛适用于科学和工程前沿，包括人脸识别，目标识别和癌症基因组学分类。提出了技术转让计划，以使经济受益。 拟议的教育计划将培养学生在统计和电气工程的跨学科领域。该项目的成功将为基础科学研究、高性能计算和信息技术带来巨大利益，并对整个社会产生重大的广泛影响。

Robotics and Computer Vision ProgramABSTRACTProposal #: 0328802Title: Nonseparable Multiclass Learning for Object TrackingPI: Shen, XiaotongOhio State Univ Res FdnObject tracking is an important technology of image and video processing for many applications, including vision-guided automation, automatic target identification, object-based video compression, and face recognition. A fundamental tool that underlies the technology of object tracking is machine classification. Recent developments of binary psi-learning allow us to further achieve higher generalization accuracy for nonseparable and multiclass cases. This proposal presents an interdisciplinary research plan to address the problem of multiple object-tracking via this new learning tool. The project will investigate how the accuracy of psi-learning can be maximized by studying its generalization ability as well as methods for assessment. Specific desired outcomes of the project are (a) creation of a stable foundation for psi-learning and further development of learning theory, optimization theory, and algorithms, and (b) specific development of mechanisms for object tracking and extraction in multimedia compression.The proposed project is expected to have broad impacts to education, research, economy, and society at large. In particular, the technology developed in this project is widely applicable to scientific and engineering frontiers, including face-recognition, target identification, and cancer genomics classification. Plans for technology transfer are proposed to benefit the economy. The proposed educational program will train students in an interdisciplinary area of statistics and electrical engineering. Success of this project will bring tremendous benefits to fundamental scientific research, high-performance computing, and information technology, and have significant broad impacts to society at large.