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

• URL: http://arxiv.org/abs/2511.20716v1
• Date: Tue, 25 Nov 2025 04:54:51 GMT
• Title: Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?
• Authors: Kun Guo, Yun Shen, Xijun Wang, Chaoqun You, Yun Rui, Tony Q. S. Quek,
• Abstract summary: Recent advances in mobile edge computing have made it possible to offload-intensive object detection to edge servers equipped with high-accuracy neural networks.<n>This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking.<n>We propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection.
• Score: 57.000348519630286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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