Omni-directional Feature Learning for Person Re-identification
This work improves person re-identification for intelligent surveillance systems, but it is incremental as it builds on existing part-based deep models by adding vertical features and spatial considerations.
The paper tackled the problem of person re-identification by addressing the neglect of vertical direction features and spatial information in part-based models, resulting in a multi-branch deep model that outperformed state-of-the-art methods on benchmarks like CUHK03, Market-1501, and DukeMTMC-reID.
Person re-identification (PReID) has received increasing attention due to it is an important part in intelligent surveillance. Recently, many state-of-the-art methods on PReID are part-based deep models. Most of them focus on learning the part feature representation of person body in horizontal direction. However, the feature representation of body in vertical direction is usually ignored. Besides, the spatial information between these part features and the different feature channels is not considered. In this study, we introduce a multi-branches deep model for PReID. Specifically, the model consists of five branches. Among the five branches, two of them learn the local feature with spatial information from horizontal or vertical orientations, respectively. The other one aims to learn interdependencies knowledge between different feature channels generated by the last convolution layer. The remains of two other branches are identification and triplet sub-networks, in which the discriminative global feature and a corresponding measurement can be learned simultaneously. All the five branches can improve the representation learning. We conduct extensive comparative experiments on three PReID benchmarks including CUHK03, Market-1501 and DukeMTMC-reID. The proposed deep framework outperforms many state-of-the-art in most cases.