Models and Algorithms for Detection and Tracking of Coordinated Groups

S. K. Pang, Jack Li, S. Godsill
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引用次数: 47

Abstract

In this paper, we describe a set of models and algorithms for detection and tracking of group and individual targets. We develop a novel group dynamical model within a continuous time setting and a group structure transition model. This is combined with an interaction model using Markov Random Fields (MRF) to create a realistic group model. We use a Markov Chain Monte Carlo (MCMC)-Particle Algorithm to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets, as well as infer the correct group structure.
协同群体检测与跟踪的模型与算法
在本文中,我们描述了一套用于检测和跟踪群体和个体目标的模型和算法。我们建立了一个新的连续时间设定下的群体动力学模型和群体结构转移模型。这与使用马尔可夫随机场(MRF)的交互模型相结合,以创建一个现实的群体模型。我们使用马尔可夫链蒙特卡罗(MCMC)-粒子算法来执行顺序推理。计算机仿真结果表明,该算法具有探测和跟踪目标的能力,并能推断出正确的群结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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