Where to look: Multi-granularity occlusion aware for video person re-identification

Video person re-identification(re-ID) plays an important role in intelligent video surveillance, which can automatically match the same person across video clips under non-overlapping cameras. Despite great progress in re-ID, the performance of most existing methods still is corrupted severely under partial occlusion. Although some multi-granularity methods have alleviated this dilemma, these methods still suffer from weak diversity of features and conflict between rigid horizontal partition and vertical occlu-sion. In this paper, we propose a novel video person re-ID framework, called Multi-Granularity Occlusion Aware (MGOA), which extracts multi-granularity features by precisely erasing the occlusion. Different from previous works based on multiple granularities, the proposed MGOA predicts the partial occlusion in a coarse-to-fine manner instead of erasing the occlusion in video clips by one step. Specifically, we first propose the multi-granularity feature extraction to obtain diverse features at different levels of feature maps, which is beneficial for the fine erasure of the occlusion. Moreover, to avoid the limitation of hor-izontal stripes that cannot handle vertical occlusion, we design Attention-Aware Occlusion Erasure (AA-OE) that can obtain the attention maps with coarse occlusion erasure in the coarse-grained branch and the attention maps with fine occlusion erasure in the fine-grained branch. It is worth noting that each granularity in our network is not independent but relevant through the top-down information transmis-sion between granularities, which transfers the erased occlusion feature maps of the current branch to the next finer-grained branch for guiding AA-OE to obtain more discriminative features. Extensive exper-iments on three challenging public benchmarks show that our MGOA can deal well with occlusion and achieves state-of-the-art performance. (c) 2023 Elsevier B.V. All rights reserved.