ABSTRACT Crowd counting plays an important role in urban management and public security. Recently, deep learning has shown a great advantage in making the quality of crowd counting more accurate. However, how to apply deep learning models to embedded terminals is still a challenging issue. The main contradiction of the problem lies in high demand for computing resources for deep learning and strict limitation of power consumption from embedded devices. In order to achieve the crowd counting by edge computing (in embedded terminals), we propose a tiny model based on convolutional neural networks. The model can be switched into other two forms to adapt tradeoff between the accuracy and efficiency. Especially, different forms share main parameters so as to save storage and avoid retraining. What is more, this model supports variant sizes of input images, which benefits the applications to a variety of embedded devices. The experiments on two different embedded terminals have shown the effectiveness, efficiency, and flexibility of our proposed model.
Chen et al. REF proposed an accurate and compact model based on CNNs, which could be deployed in embedded devices.
Efficient and Switchable CNN for Crowd Counting Based on Embedded Terminal
IEEE Access
IEEE Access
Computer Science