Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?

TL;DR

This paper presents LTED-Ada, a deep reinforcement learning-based method for adaptive video object recognition in mobile edge networks, balancing local tracking and edge detection to optimize accuracy and delay.

Contribution

It introduces a novel adaptive algorithm using deep reinforcement learning and federated learning for dynamic decision-making in resource-constrained video recognition.

Findings

LTED-Ada outperforms baseline methods in accuracy and delay.

Federated learning enhances generalization across devices.

Extensive experiments validate the approach on Raspberry Pi and edge server setups.

Abstract

Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent advances in mobile edge computing have made it possible to offload computation-intensive object detection to edge servers equipped with high-accuracy neural networks, while lightweight and fast object tracking algorithms run locally on devices. This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking. To address this, we formulate two long-term optimization problems for both single-device and multi-device scenarios, taking into account the temporal correlation of consecutive frames and the dynamic conditions of mobile edge networks. Based on the formulation, we propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection, according to the frame rate as well as recognition accuracy and delay requirement. In multi-device setting, we further enhance LTED-Ada using federated learning to enable collaborative policy training across devices, thereby improving its generalization to unseen frame rates and performance requirements. Finally, we conduct extensive hardware-in-the-loop experiments using multiple Raspberry Pi 4B devices and a personal computer as the edge server, demonstrating the superiority of LTED-Ada.