Singh, Shubham KumarShubham KumarSinghMiyapuram, Krishna P.Krishna P.MiyapuramRaman, ShanmuganathanShanmuganathanRaman2025-08-312025-08-312020-01-01[9789811586965]10.1007/978-981-15-8697-2_372-s2.0-85097257059http://repository.iitgn.ac.in/handle/IITG2025/24309Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep Parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets.falseArchitecture | Deep learning | Person re-identificationConference Paperhttps://arxiv.org/pdf/1911.0777618650937397-40720200cpBook Series0