An Empirical Study of Person ReIdentification with Attributes Person reidentification aims to identify a person from an image collection, given one image of that person as the query. There is, however, a plethora of reallife scenarios where we may not have a priori library of query images and therefore must rely on information from other modalities. In this paper, an attributebased approach is proposed where the person of interest (POI) is described by a set of visual attributes, which are used to perform the search. We compare multiple algorithms and analyze how the quality of attributes impacts the performance. While prior work mostly relies on high precision attributes annotated by experts, we conduct a humansubject study and reveal that certain visual attributes could not be consistently described by human observers, making them less reliable in real applications. A key conclusion is that the performance achieved by nonexpert attributes, instead of expertannotated ones, is a more faithful indicator of the status quo of attributebased approaches for person reidentification.

Partial AttributeDriven Video Person ReIdentification Person reidentification has gradually become a hot research topic in many fields, such as security, criminal investigation and video analysis. In this paper, we propose a novel feature extraction framework for videobased person reidentification, namely, the partial attributedriven network (PADNet). The proposed method is based on the deeplearning architecture and incorporates the attribute and identity learning of the pedestrian. Existing attribute research always focuses on the feature representation at the globallevel. Unlike them, first, the pedestrian is automatically partitioned to several body parts in our work. Then the pedestrian and hisher body parts are annotated by the global and partial attributes, respectively. Finally, we employ a fourbranch multilabel network to explore the spatialtemporal cues of videos by utilizing these labeled samples. Extensive experiments are conducted on two videobased datasets, including PRID2011 and iLIDSVID. The experimental results demonstrate the superiority and effectiveness of the proposed PADNet over the stateoftheart approaches.