CogVSM framework

This project implements a hierarchical edge computing system with a Long Short-Term Memory (LSTM) model for cognitive video surveillance management, as described in the paper titled "Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model".

Overview

The system consists of two edge nodes: the first edge node and the second edge node. The first edge node handles object detection using the YOLOv7-tiny model and communicates with the second edge node for further processing. The second edge node predicts future object occurrences using LSTM, controls threshold values, and performs motion tracking.

Edge Node Configuration

• First Edge Node (Client Side): Responsible for object detection using YOLOv7-tiny model. Utilizes Jetson Nano with an ARM A56 CPU and NVIDIA Maxwell GPU. Connected to an IP camera via USB.

• Second Edge Node (Server Side): Conducts future object occurrence prediction with LSTM, controls threshold values, and implements motion tracking using TF-pose-estimation.

Performance

While motion tracking on the Jetson Nano proved too slow for real-time monitoring, our hierarchical edge computing system optimizes performance by offloading motion tracking to the second edge node.

For more details, refer to the original paper.