MotionSearch: Motion Trajectory-Based Object Activity Retrieval and Recognition from Video and Sensor Databases

运动是代表各种传感器设备的时间数据集中的重要组成部分。 本项目研究设计的可扩展的运动内容为基础的索引/检索机制和活动识别系统的应用程序，采用大型运动数据档案。 开发了一个统一的数学框架来表示运动特征，允许集成索引/检索公式以及基于语义的智能识别系统。 运动轨迹分割成子轨迹使用计算效率高的技术，利用曲率信息和科普闭塞和丢失的数据。 子轨迹的表示是基于主成分分析（PCA）的运动数据，它允许紧凑的低维表示以及实时查询处理。 这种表示扩展到多个运动轨迹数据是基于三维张量奇异值分解（SVD）。 这些分析动机的特征空间的创新使用依赖于开发强大的索引和检索系统和可扩展的活动识别系统的基础上隐马尔可夫模型。 开发的技术原型和系统的性能进行评估，使用几个视频档案。 该项目是一个巨大的进步，统一查询的例子为基础的索引和检索系统和高层次的语义查询为基础的活动识别系统。 该项目将显著提高基于内容的索引和检索以及活动识别系统的当前技术水平，以应用于使用时态数据集的应用。 它将促进娱乐和安全应用程序的各种基于运动的应用程序的开发。 该项目的网站（http：//multimedia.ece.uic.edu/motionsearch）可供查阅所产生的研究论文和执行代码。

Motion is an important component in temporal datasets representing a variety of sensor devices. This project investigates the design of scalable motion-content-based indexing/retrieval mechanisms and an activity recognition system for applications that employ large motion data archives. A unified mathematical framework is developed for representing motion features that allows integration of indexing/retrieval formulation as well as semantic-based intelligent recognition systems. Motion trajectories are segmented into sub-trajectories using computationally efficient techniques that exploit curvature information and cope with occlusions and missing data. Representation of sub-trajectories is based on principle component analysis (PCA) of the motion data which allows compact low-dimensional representation as well as real-time query processing. Extension of this representation to multiple motion trajectory data is based on three-dimensional tensor singular value decomposition (SVD). Innovative use of these analytically-motivated feature spaces is relied upon for developing robust indexing and retrieval systems and scalable activity recognition systems based on Hidden Markov Models. The techniques developed are prototyped and the performance of the system is evaluated using several video archives. The project is a giant step forward towards unifying query-by-example-based indexing and retrieval systems and high-level semantic query-based activity recognition systems. This project will significantly enhance the current state of the art in content-based indexing and retrieval and activity recognition systems for applications that employ temporal datasets. It will facilitate the development of diverse motion-based applications for entertainment and security applications. The project web site (http://multimedia.ece.uic.edu/motionsearch) provides access to resulting research papers and implementation code.