Multistage Multiscale Inference Network with Visibility Attention for Occluded Person Re-Identification

For occluded person re-identification this thesis presents the Multistage Multiscale Inference Network (MMI-Net) that leverages an inference framework based on multiscale representations with visibility guidance. MMI-Net consists of three sub-networks, i) global, ii) part-based and iii) integrated, to infer person re-identification. The global inference sub-network provides an overall holistic analysis of input images. The part-based sub-network captures more localized information. Both the global and part-based models make use of multiscale representation across multiple processing stages to capture a variety of complementary discriminative image structure. The integrated sub-network aggregates the global and part-based representations to obtain the final fusion of all extracted information. Pose guided attentional processing is used to provide robustness to occlusion. MMI-Net is unique in its integrated multistage inference architecture that accounts for local and global appearance with attentional processing. In empirical evaluation, MMI-Net outperforms current existing methods on multiple occluded person re-identification datasets.