Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference
The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditional approaches require massive video data to be transmitted from resource-constrained edge devices to powerful cloud servers, incurring prohibitive uplink bandwidth consumption and unacceptable latency while raising privacy concerns. To overcome these bottlenecks, we propose a task-oriented communication framework for human action understanding (TOAU) through edge-cloud collaboration. Our framework utilizes a monocular pose estimator to extract continuous joint coordinates from raw videos, followed by a vector quantized variational autoencoder...
| Authors | Jingyi Liu, Cheng Yuan, Lijun He et al. |
|---|---|
| Primary category | eess.SP |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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