Scalable Solutions for Efficient Real-Time Distributed Video Analytics with Vehicle Detection on CPU Edge Nodes
Shekhar Khadka, Sharad Kumar Ghimire · 2024
Traditional video analytics are typically performed on a single node having limited processing power and vertical scalability, resulting in a lack of real-time performance and potential single point failures. To address these issues, this research pioneers a distributed video processing hosted on Microsoft Azure Cloud, employing Apache Kafka distributed messaging framework, and optimizing the YOLOv8 model for performing real-time video analytics in CPU edge nodes. The study explores two optimization strategies, i.e., post-training optimization with magnitude pruning and pre-training optimization using Open VINO. Through meticulous experimentation, the research delves into the nuanced relationship between model complexity, object detection accuracy, and real-time processing, crucial for resource-constrained environments. After in-depth experimentation, the pre-training optimization not only enhances accuracy but also significantly improves processing speed, rendering it particularly well-suited for real-time scenarios. The Kafka-based infrastructure not only ensures fault tolerance but also exhibits scalability, providing reliable distributed video processing. This research establishes the significance of distributed video analytics for real-time applications, offering insights into optimal configurations in lower-end CPU devices.