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Lightweight Person Re-Identification for Edge Computing
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AI
DOI：10.1109/ACCESS.2024.3405169.png)
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摘要
En 中文
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.
Keyword:
Identification of persons
Feature extraction
Knowledge engineering
Computational modeling
Pedestrians
Edge computing
Tensors
Dimensional attention mechanism
edge computing
lightweight network
person re-identification
self-distillation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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