Pose-Guided Feature Alignment for Occluded Person Re-Identification
2019 IEEE/CVF International Conference on Computer Vision (ICCV)(2019)
摘要
Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions during feature generation and matching. In this paper, we introduce a novel method named Pose-Guided Feature Alignment (PGFA), exploiting pose landmarks to disentangle the useful information from the occlusion noise. During the feature constructing stage, our method utilizes human landmarks to generate attention maps. The generated attention maps indicate if a specific body part is occluded and guide our model to attend to the non-occluded regions. During matching, we explicitly partition the global feature into parts and use the pose landmarks to indicate which partial features belonging to the target person. Only the visible regions are utilized for the retrieval. Besides, we construct a large-scale dataset for the Occluded Person Re-ID problem, namely Occluded-DukeMTMC, which is by far the largest dataset for the Occlusion Person Re-ID. Extensive experiments are conducted on our constructed occluded re-id dataset, two partial re-id datasets, and two commonly used holistic re-id datasets. Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.
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关键词
person retrieval scenarios,previous person re-identification,occlusion problem,occluded regions,feature generation,occlusion noise,feature constructing stage,attention maps,nonoccluded regions,pose landmarks,partial features,visible regions,Occluded-DukeMTMC,re-id dataset,person re-id methods,occlusion datasets,occluded person reidentification,occlusion person Re-ID,occluded person Re-ID problem,pose-guided feature alignment
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