RT-VeD: Real-Time VoI Detection on Edge Nodes with an Adaptive Model Selection Framework

Shuai Wang, Junke Lu, Baoshen Guo, Zheng Dong

Abstract: Real-time Vehicle-of-Interest (VoI) detection is becoming a core application to smart cities, especially in areas with high accident rates. With the increasing number of surveillance cameras and the advanced developments in edge computing, video tasks prefer to run on edge devices close to cameras due to the constraints of bandwidth, latency, and privacy concerns. However, resource-constrained edge devices are not competent for dynamic traffic loads with resource-intensive video analysis models. To address this challenge, we propose RT-VeD, a real-time VoI detection system based on the limited resources of edge nodes. RT-VeD utilizes multi-granularity computer vision models with different resource-accuracy trade-offs. It schedules vehicle tasks based on a traffic-aware actor-critic framework to maximize the accuracy of VoI detection while ensuring an inference time-bound. To evaluate the proposed RT-VeD, we conduct extensive experiments based on a real-world vehicle dataset. The experiment results demonstrate that our model outperforms other competitive methods.