End-to-End Deep Kronecker-Product Matching for Person Re-identification
This work improves person re-identification for surveillance and security applications, but it is incremental as it builds on existing deep learning methods with novel modules.
The paper tackles the problem of person re-identification by addressing variations in poses and viewing angles, proposing an end-to-end deep Kronecker-Product Matching module with feature soft warping, which outperforms state-of-the-art methods on datasets like Market-1501, CUHK03, and DukeMTMC.
Person re-identification aims to robustly measure similarities between person images. The significant variation of person poses and viewing angles challenges for accurate person re-identification. The spatial layout and correspondences between query person images are vital information for tackling this problem but are ignored by most state-of-the-art methods. In this paper, we propose a novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network. A novel feature soft warping scheme is designed for aligning the feature maps based on matching results, which is shown to be crucial for achieving superior accuracy. The multi-scale features based on hourglass-like networks and self-residual attention are also exploited to further boost the re-identification performance. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach.