Estimating crowd density with edge intelligence based on lightweight convolutional neural networks

计算机科学 卷积神经网络 GSM演进的增强数据速率 人工智能 机器学习 人工神经网络 模式识别(心理学)
作者
Shuo Wang,Ziyuan Pu,Qianmu Li,Yinhai Wang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:206: 117823-117823 被引量:25
标识
DOI:10.1016/j.eswa.2022.117823
摘要

• Computing on edge end improves the efficiency and reliability of data analysis. • A lightweight CNN model is efficient for real-time crowd density estimation on edge. • Better crowd density inference speed with a slight increase in estimation accuracy. • Equip the model in an IoT device to monitor the crowd density in a Subway Station. Crowd stampedes and incidents are critical threats to public security that have caused countless deaths during the past few decades. To avoid crowd stampedes, real-time crowd density estimation can help monitor crowd movements, and thus support a timely evacuation strategy development. In previous studies, scholars and engineers developed multiple video-based crowd density estimation algorithms based on deep neural networks. The excessive computational complexity of deep learning algorithms exacerbated the algorithm’s efficiency, causing unacceptable real-time performance. In the Internet of Things era, deploying the crowd density estimation task with edge computing is an advanced strategy to maintain the real-time performance of the entire system. Considering the limited computational resources on the edge devices, deep learning-based crowd density estimation algorithms normally cannot be handled. To fulfill the deployment on the edge device, the algorithms need to be optimized with a smaller model size. Therefore, this paper proposes a lightweight Convolutional Neural Networks (CNN) based crowd density estimation model by combining the modified MobileNetv2 and the dilated convolution. Public crowd image data sets are used to conduct experiments for evaluating the performance of the proposed algorithm in terms of accuracy and inference speed. The results show that our model achieves much better inference speed accompanied by a slight increase in accuracy. The proposed method of this study can enhance the performance of the crowd monitoring system, and therefore help avoid crowd stampedes and incidents.
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