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

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.