人群自动计数问题在视频监控领域引起了广泛关注。近年来，卷积神经网络 (CNN)模型在人群计数方面取得了良好效果。然而，当前对于基于深度学习的人群计数的研究主要停留在 PC端上对单幅静止图片的人群计 数，网络模型参数量巨大，网络结构复杂，消耗的计算资源巨大，难以部署于实际的监控视频人群计数系统。因此，本文采用深度学习的方法，通过对网络模型进行裁剪压缩，同时使用 TensorRT对模型进行加速，在嵌入式 平台上实现了接近实时的人群计数。提出的人群计数平均绝对误差 (MAE)为 21.6且平均每秒帧数 (FPS)为 22，在精确度和速度方面达到了一个很好的平衡，在嵌入式平台上运行速度较快，能达到实时的效果。

Automatic crowd counting has attracted widespread concern in the field of video surveillance. In recent years, the Convolutional Neural Network(CNN) has achieved miraculous results in crowd counting. However, current research based on deep learning mainly concentrates on high-performance PC to count the people with a single still picture. The network model has huge computational resources consuming due to its large amount of parameters and complex network structure, which is difficult to deploy in actual surveillance video crowd counting system. Therefore, the deep learning method is adopted to realize the real-time crowd counting on the embedded platform by pruning and compressing the network model and using TensorRT to accelerate the model inference. The proposed crowd counting algorithm achieves a balance between accuracy and speed with Mean Absoulte Error(MAE) of 21.6 and average Frames Per Second(FPS) of 22. Its performance on the embedded platform can approach the real-time result.