A Practical Person Monitoring System for City Security

摘要

Recent progress in Deep Learning(DL) has brought many breakthroughs with incredible performance, which have not been achieved with traditional machine learning algorithms. In computer vision, DL-based methods have started to outperform humans in certain tasks and are going to impact our daily lives. We present our case study of an implementation and evaluation of our prototype real-time person-monitoring system using cutting-edge DL computer vision techniques. We used a fast and lightweight stream-processing engine for its flexibility and portability, packaged all of DL software stacks as docker containers for portability and ease of deployment, and evaluated our prototype's performance using realistic scenarios in which one hundred camera streams are gathered at centered GPU servers. We confirmed that our prototype system can monitor one hundred video streams in real-time. We also report lessons learned through our prototype implementation and discuss the future direction of person monitoring.