Edge Camera System Using Dee p Learning Method with Model Compression on Embedded Applications

This paper proposes an edge camera system using a compressed deep learning model for enhanced Video surveillance Management System of Smart City that analyzes existing recorded video. The proposed edge camera at the end terminal of the Video Surveillance Management System send the analyzed image, information and warning to the central system according to the situation analysis based on the information obtained by compressed deep learning for low memory and real-time operation in embedded system. We tested with edge camera installed in street lamp and confirmed stable operating performance and high recognition rate compared to existing system.