DeepPFCN: Deep Parallel Feature Consensus Network For Person Re-Identification

TL;DR

DeepPFCN introduces a multi-scale convolutional neural network architecture that learns robust, scale-specific person appearance features for improved automated person re-identification across multiple camera views.

Contribution

The paper proposes DeepPFCN, a novel deep network that factorizes and fuses multi-scale appearance features, enhancing re-identification accuracy over existing methods.

Findings

Achieved state-of-the-art mAP scores on three benchmark datasets.

Effectively learns discriminative scale-specific features.

Demonstrated robustness of features for person re-identification.

Abstract

Person 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.