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Lightweight Person Re-Identification for Edge Computing
- Source :
- IEEE Access, Vol 12, Pp 75899-75906 (2024)
- Publication Year :
- 2024
- Publisher :
- IEEE, 2024.
-
Abstract
- In person re-identification, most prevalent models are predominantly designed for cloud computing environments which introduces complexities that limit their effectiveness in edge computing scenarios. Person re-identification systems optimized for edge computing can achieve real-time or near-real-time responses, providing substantial practical value. Addressing this gap, this paper presents the Attention Knowledge-aided Distillation Lightweight Network (ADLN), a network architecture expressly crafted for edge computing. The ADLN enhances inference speed while maintaining accuracy, which is essential for real-time applications. The core innovation of the ADLN lies in its dimension interaction attention mechanism, strategically integrated into the network to boost recognition performance. This mechanism is complemented by a self-distillation approach, transferring attention knowledge from deeper to shallower layers, thereby streamlining the network and accelerating inference. Moreover, the ADLN employs an optimization strategy combining cross-entropy loss, weighted triplet loss regularization, and center loss, effectively reducing intra-class variances. Tested on Market1501 and DukeMTMC-ReID datasets, experiments indicate that the ADLN significantly reduces the model’s parameter count and identification latency, while largely maintaining accuracy.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2b828e526d9146ab9e3cee7ca6836b79
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2024.3405169