In this paper, we propose a novel people counting algorithm exploiting convolutional neural network (CNN) using a low radiation impulse radio ultra-wide bandwidth (IR-UWB) radar. Because of the ever-changing signals caused by the various cases of human motion scales, superposition and obstruction of signals as well as the attenuate of signal's strength along the distance and the angle, it is not easy to handle the people counting task by directly detecting targets for each range bin. Thus, we hope to excavate the information of targets' patterns, including their densities and forms of patterns' distributions in the detecting region to execute the counting task. To achieve this, the multi-scale range-time maps are extracted from the received data and further used to classify the number of people using the CNN. Finally, the experiments are conducted to show the priority of the proposed algorithm.

中文翻译：

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基于 CNN 的区域人数统计算法，利用 IR-UWB 雷达利用多尺度范围时间地图


在本文中，我们提出了一种利用低辐射脉冲无线电超宽带（IR-UWB）雷达的卷积神经网络（CNN）的新型人数计数算法。由于人体运动尺度的各种情况、信号的叠加、遮挡以及信号强度随距离和角度的衰减而导致信号不断变化，直接检测来处理人数统计任务并不容易每个范围箱的目标。因此，我们希望挖掘目标图案的信息，包括检测区域中目标图案的密度和图案分布的形式来执行计数任务。为了实现这一目标，从接收到的数据中提取多尺度范围时间图，并进一步使用 CNN 对人数进行分类。最后通过实验验证了所提算法的优先级。