Abstract:

Person re-identification (re-ID) involves the cross-camera retrieval and matching of target pedestrian images, facilitating pedestrian association in scenarios where biometric features such as faces and fingerprints may prove ineffective. It has become a pivotal technology in intelligent video surveillance systems, playing a crucial role in domains like smart security and smart cities. Traditional re-ID algorithms typically employ either representation learning or metric learning methods. A novel approach was proposed which combined representation learning and metric learning methods based on the multi-task learning machine learning paradigm. By capitalizing on the advantages of both feature representation and distance metric, and concurrently training the model using classification loss and triplet loss, comprehensive training for both feature extraction and similarity measurement was ensured. Experimental results validate the effectiveness of the proposed approach, demonstrating superior performance in re-ID tasks and underscoring the robustness and superior generalization capability.

Key words: person re-identification, intelligent video surveillance, representation learning, metric learning, multi-task learning