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首页> 外文期刊>Infrared physics and technology >A novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system: Region partition using PNN and bearing-crossing location
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A novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system: Region partition using PNN and bearing-crossing location

机译：分布式二进制热释电红外传感器跟踪系统的多人定位新方法：基于神经网络和方位交叉定位的区域划分

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This paper proposes a novel multi-human location method for distributed binary pyroelectric infrared sensor tracking system based on region partition using probabilistic neural network and bearing-crossing location. The detection space of system is divided into many sub-regions and encoded uniformly. The human region is located by an integrated neural network classifier, which is developed based on the probabilistic neural network ensembles and the Bagging algorithm. The location of a human target can be achieved by first determining a coarse location by this classifier and then a fine location using our previous bearing-crossing location method. Simulation and experimental results have shown that the human region can be judged rapidly and the false detection points of multi-human location can be eliminated effectively. Compared with the bearing-crossing location method, the novel method has significantly improved the locating and tracking accuracy of multiple human targets in infrared sensor tracking system. (C) 2014 Elsevier B.V. All rights reserved.
机译：提出了一种基于概率神经网络和方位交叉定位的区域划分的分布式二进制热释电红外传感器跟踪系统的多人定位新方法。系统的检测空间分为多个子区域，并进行统一编码。人类区域由集成的神经网络分类器定位，该分类器是基于概率神经网络集合和Bagging算法开发的。可以通过首先使用该分类器确定粗略位置，然后使用我们之前的方位交叉定位方法确定精细位置来实现人类目标的位置。仿真和实验结果表明，可以快速判断人的区域，有效消除多人位置的虚假检测点。与跨轴定位方法相比，该新方法显着提高了红外传感器跟踪系统中多个人体目标的定位和跟踪精度。 （C）2014 Elsevier B.V.保留所有权利。

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