Person Re-Identification using Unsupervised Learning and ResNets in Surveillance Systems: A Progressive Clustering-based Learning Approach
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- DOI码：10.15680/IJIRCCE.2025.1303007
- 发表刊物：IJIRCCE
- 关键字：Person re-identification; unsupervised learning; Progressive Clustering-Based Learning; ConfidenceBased
Sample Selection; ResNet-50
- 摘要：Unsupervised person re-identification (re-ID) is a deep learning task aimed at extracting distinctive
features without the need for labeled data, this task is crucial for practical applications in surveillance systems. Recent
advancements in re-ID have shown the effectiveness of deep learning techniques in learning robust pedestrian
representations. However, training deep models typically requires large-labeled datasets, which are both costly and
impractical for real-world deployment. To overcome this challenge, we propose an Unsupervised Learning framework
that transfers pre-trained deep representations to unseen domains with minimal reliance on labeled data. Our approach
alternates between pseudo-labeling through image clustering and CNN fine-tuning, progressively refining feature
representations. Additionally, we incorporate an advanced feature extraction strategy and k-means clustering for
pseudo-labeling, ensuring robust unsupervised training. Extensive experiments on large-scale re-ID benchmarks,
including Market-1501 and DukeMTMC-reID`, demonstrate that our method outperforms baseline models, achieving
superior accuracy in both supervised and unsupervised settings. This framework offers a scalable and efficient solution
for real-world person re-ID applications, effectively bridging the gap between supervised and unsupervised learning
while significantly reducing annotation costs.
- 第一作者：文学志
- 论文类型：期刊论文
- 通讯作者：Maambo Lubinda
- 卷号：13
- 期号：3
- 是否译文：否
- 发表时间：2025-01-25
- 文学志.pdf 下载[] 次
