Occluded Person Retrieval with Hierarchical Feature Optimization

Occluded person retrieval aims to match images from occluded pedestrians. It pushes forward progress of person retrieval towards applications in real-world scenarios, thus attracting increasing attention in recent years. A key challenge is to learn discriminative representation within limited informative regions due to obstacle or pedestrian occlusion. To that end, we propose a hierarchical feature optimization model (HFO) that jointly optimizes image-level, object-level and part-level features for improved occluded person retrieval. A hierarchical discriminative feature grouping (HDFG) module is developed to generate hierarchical object/part masks for comprehensive feature extraction. Via learning a set of part prototypes, HDFG localizes hierarchical informative object/parts by grouping intermediate feature vectors based on their similarity to these prototypes. The proposed HFO is trained in an end-to-end manner using only identity labels, making it a practical solution for occluded person retrieval. We verify the effectiveness of the proposed method on three challenging occluded datasets and two holistic datasets, i.e., Occluded-DukeMTMC, Occluded-REID, P-DukeMTMC-reID, Market1501, and DukeMTMC-reID. Extensive experiments and ablation studies demonstrate superior or comparable performance of the proposed method over the state-of-the-art methods. The code is available at https://github.com/Patrickzad/HFO.