TY  - JOUR
AU  - Baggam, Revathi Lavanya 
AU  - Kumari, Vatsavayi Valli 
PY  - 2025
TI  - Person Re-Identification From Video Surveillance Systems Using Artificial Intelligence Methods
JF  - Journal of Computer Science
VL  - 21
IS  - 8
DO  - 10.3844/jcssp.2025.1819.1833
UR  - https://thescipub.com/abstract/jcssp.2025.1819.1833
AB  - The study explores use of deep learning models in person re identification, leveraging the advancements made in face recognition however the abundance of model choices presents a challenge in selecting the optimal architecture. The study proposes a comprehensive framework for evaluating deep learning models on person re-identification tasks by considering various performance metrics, dataset preprocessing methods, model architectures, and evaluation techniques to enable a systematic comparison of different approaches through empirical analyses on standard person re-identification datasets. The proposed framework is worked-out in uncovering the strengths and limitations of diverse deep learning strategies. The primary objective is to utilize face recognition methodologies to achieve accurate person re-identification.