Research on person re-identification algorithm based on multi-task learning
Person re-identification(re-ID)involves the cross-camera retrieval and matching of target pedestrian im-ages,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 advan-tages 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.Experi-mental results validate the effectiveness of the proposed approach,demonstrating superior performance in re-ID tasks and underscoring the robustness and superior generalization capability.
person re-identificationintelligent video surveillancerepresentation learningmetric learningmulti-task learning
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