Nonseparable Multiclass Learning for Object Tracking

目标跟踪是一项重要的图像和视频处理技术，在视觉引导自动化、自动目标识别、基于对象的视频压缩和人脸识别等领域有着广泛的应用。目标跟踪技术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在不可分和多类情况下进一步实现更高的泛化精度。本提案提出了一个跨学科的研究计划，以解决多目标跟踪问题，通过这个新的学习工具。该项目将通过研究psi学习的泛化能力和评估方法来研究如何最大限度地提高psi学习的准确性。该项目的具体预期结果是(a)为psi学习和进一步发展学习理论、优化理论和算法建立一个稳定的基础，以及(b)在多媒体压缩中具体开发对象跟踪和提取机制。预计该项目将对教育、研究、经济和社会产生广泛影响。特别是，该项目开发的技术广泛应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。提出技术转让计划是为了使经济受益。拟议的教育计划将在统计学和电气工程的跨学科领域培养学生。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的利益，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.