Deep Neural Network-based Physical Distancing Monitoring System with TensorRT Optimization - Edi Kurniawan (International Journal of Advances in Intelligent Informatics, 2022)
During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. Deep learning-based object detection is employed to detect people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) with Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. The results show that the inferencing speed in terms of Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones.