Semantic Communication-Based Low-Carbon Sustainable Framework for Person Re-Identification

Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird’s eye view (BEV) of cameras. Cross-illumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based low-carbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semantics-aware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7%, and 76.6%, respectively.