Comparative study of person re-identification techniques based on deep learning models

Person re-identification (Re-ID) is crucial in intelligent surveillance, requiring precise identification of individuals across multiple camera viewpoints. Traditional distance-based methods, such as Euclidean and Cosine, struggle with challenges like posture variations and occlusions, limiting their effectiveness. This study explores deep metric learning models, specifically Siamese and Triplet networks, to improve Re-ID performance. We evaluate these methods on the Market-1501 dataset using Cumulative Matching Characteristic (CMC) and Cumulative Distribution Function (CDF) curves. Our findings reveal that the Triplet network outperforms traditional approaches at higher ranks, achieving Rank-5 accuracy of 78.6% and Rank-10 accuracy of 93%, while its Rank-1 accuracy remains low (0.06%). In contrast, Euclidean and Cosine distances show poor Rank-1 performance (2% and 0.30%, respectively), highlighting their limitations. Additionally, incorporating VGG16 enhances feature extraction, improving recognition by capturing fine-grained spatial details. This comparative study highlights the effectiveness of deep metric learning and underscores its potential for real-world surveillance applications. However, the computational demands of deep networks present challenges for real-time deployment. Future research should focus on optimizing model efficiency, reducing computational costs, and extending evaluations to real-time scenarios.