A bidirectional fusion branch network with penalty term-based trihard loss for person re-identification.

• Using low-level features for pedestrian Re-ID has significant advantages. • The bidirectional fusion branch networks can preserve low-level features effectively. • The model's clustering performance is improved through using penalty terms. • The optimized sample space has better generalization ability. Person re-identification (Re-ID) is the recognition of the same person in different camera views. Because of the existence of highly similar persons and great differences of the same person in different scenes, and the fact that the features extracted by current mainstream models lose some fine-grained information, it is likely for the models to misidentify the query person. To tackle these challenges, we introduce a bidirectional fusion branch network with penalty term-based trihard loss (BFB-PTT). The BFB-PTT constructs a bidirectional fusion branch (BFB) network based on feature pyramid, where low-level features are transferred to a high-level feature space through fewer convolutional layers than most of the traditional CNN-based models have, thus retaining more local features to discriminate different pedestrians more accurately and effectively. Meanwhile, we propose using the penalty term-based trihard loss (PTT) to optimize the spatial structure of pedestrian's samples, so that the similar samples are drawn closer together in order to reduce the variabilities of the same person in different scenes. We have conducted comprehensive experiments and analyses on the proposed method's effectiveness on three challenging benchmarks, and the results show that our approach achieves competitive performance with the state-of-art models. [ABSTRACT FROM AUTHOR]