IoT-based crowd monitoring system: Using SSD with transfer learning

Abstract

The constantly developing urbanization and the emergence of smart cities require better security surveillance and crowd monitoring systems. The growing availability of the Internet of Things (IoT) devices in public and private organizations also provide intelligent and secure surveillance solutions for real-time monitoring in public spaces. This article introduces an IoT-based crowd surveillance system that uses a deep learning model to detect and count people using an overhead view perspective. The Single Shot Multibox Detector (SSD) model with Mobilenetv2 as the basic network is used for the detection of people. The detection model’s accuracy is enhanced with a transfer learning approach. Two virtual lines are defined to count how many people are leaving and entering the scene. In order to assess performance, experiments are performed using different video clips. Results indicate that transfer learning increases the overall detection performance of the system with an accuracy of 95%.