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首页> 外文期刊>International Journal of Intelligent Systems >An Approach to Automatic Real-Time Novelty Detection, Object Identification, and Tracking in Video Streams Based on Recursive Density Estimation and Evolving Takagi-Sugeno Fuzzy Systems
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An Approach to Automatic Real-Time Novelty Detection, Object Identification, and Tracking in Video Streams Based on Recursive Density Estimation and Evolving Takagi-Sugeno Fuzzy Systems

机译：基于递归密度估计和发展的Takagi-Sugeno模糊系统的视频流自动实时新颖性检测，目标识别和跟踪

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摘要

Recently, surveillance, security, patrol, search, and rescue applications increasingly require algo rithms and methods that can work automatically in real time. This paper reports a new real-time approach based on three novel techniques for automatic detection, object identification, and track ing in video streams, respectively. The novelty detection and object identification are based on the newly proposed recursive density estimation (RDE) method. RDE is using a Cauchy-type of kernel, which is calculated recursively as opposed to the widely used (in particular in the kernel density estimation (KDE) approach) Gaussian one. The key difference is that the proposed approach works on a per frame basis and does not require a window (usually of size of several dozen) of frames to be stored in the memory and processed. It should be noted that the new RDE approach is free from user- or problem-specific thresholds by differ from the other state-of-the-art approaches. Finally, an evolving Takagi-Sugeno (eTS)-type fuzzy system is proposed for tracking. The proposed approach has been compared with KDE and Kalman filter (KF) and has proven to be significantly (in an order of magnitude) faster and computationally more efficient than RDE and more precise than KF.
机译：最近，监视，安全，巡逻，搜索和救援应用越来越需要可以实时自动运行的算法和方法。本文报告了一种基于三种新颖技术的实时方法，分别用于自动检测，对象识别和视频流跟踪。新颖性检测和对象识别基于新提出的递归密度估计（RDE）方法。 RDE使用的是柯西类型的内核，与广泛使用的（特别是在内核密度估计（KDE）方法中）高斯算法相反，它是递归计算的。关键的区别在于，所提出的方法在每个帧的基础上工作，并且不需要将窗口（通常为几十个大小）的帧存储在内存中并进行处理。应该注意的是，新的RDE方法与其他现有技术方法不同，它没有针对用户或问题的特定阈值。最后，提出了一种演化的高木苏格诺（eTS）型模糊系统进行跟踪。所提出的方法已与KDE和卡尔曼滤波器（KF）进行了比较，并被证明比RDE显着（数量级）更快，计算效率更高，并且比KF更精确。

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  • 来源
    《International Journal of Intelligent Systems》 |2011年第3期|p.189-205|共17页
  • 作者

    Plamen Angelov; Pouria Sadeghi-Tehran; Ramin Ramezani;

  • 作者单位

    School of Computing and Communications, InfoLab21, Lancaster University, Lancaster, LA1 4WA, UK;

    rnSchool of Computing and Communications, InfoLab21, Lancaster University, Lancaster, LA1 4WA, UK;

    rnSchool of Computing and Communications, InfoLab21, Lancaster University, Lancaster, LA1 4WA, UK;

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