基于WiFi交错信号与深度神经网络的室内人群数量检测方法

Indoor crowd counting method based on WiFi crossover signals and deep neural network

针对现有室内人群数量检测方法存在适用场景范围受限、检测精度低等问题,提出一种基于深度神经网络的人群数量检测方法,无需被检测人员携带设备便可实现区域内人群数量检测。该方法采用多个Wi Fi传感节点覆盖室内区域,节点间通过相互探测信号获得交错Wi Fi链路数据;运用深度神经网络进行特征学习,提取人数变化对Wi Fi信号影响的关联特征,训练得到该区域人群数量感知模型;将实时采集的Wi Fi信号送入该模型即可获得人群数量的估计。采用所提方法在一个较为复杂的室内环境进行了实验测试,结果表明该方法能够准确实现室内人数检测,检测精度达到82. 23%,平均误差仅为0. 37人;与现有其他机器学习算法相比,该模型具备更高的检测精度,适用于多种应用场景。

The existing indoor crowd counting methods face the problems limited scenarios,and low detection accuracy,etc.A crowd counting method based on deep neural networks without carrying equipment is proposed in this study.Multiple wireless fidelity(WiFi)sensor nodes are employed to cover indoor areas.The crossover WiFi link data are obtained by detecting signals among sensor nodes.Deep neural network is utilized to learn and extract the features of the effect of the change of the indoor crowd number on WiFi signals.The crowd counting model is trained for the indoor area,and it can be used to estimate the number of crowd by inputting real-time WiFi signals into the model.Evaluation experiments are implemented in a complex indoor office environment.Results show that the proposed method can realize accurate crowd counting with an accuracy of 82.23%and the mean error of 0.37 people.Compared with other machine learning methods,the deep neural network perception model has higher detection accuracy and can be applied to various application scenarios.