An Edge Video Analysis Solution For Intelligent Real-Time Video Surveillance Systems

Abstract

Video Analytics has played an essential role in the most varied public safety sectors, mainly when applied to Intelligent Video Surveillance Systems. In this scenario, Edge Video Analytics seeks to migrate part of the workload of the Video Analysis process to devices close to the data source to reduce transmission overhead on the network and overall latency. Therefore, this work proposes an Edge Video Analytics architecture for real-time video monitoring systems. Such architecture divides the analysis process into functional and independent modules, being flexible to support analytics or network functions. We developed a proof of concept to validate the proposed architecture, focusing on detecting and recognizing license plate characters in the edge. In this scenario, between the detection and recognition modules, we used Deep Learning to implement a module responsible for discard plates with distorted identification text to reduce network utilization. Conducted experiments demonstrate that the architecture meets its objectives by reducing an average of 25.64% of the network traffic in its frames flow due to a resolution quality control and 56.65% in its license plate flow due to the filtering step proposed.