A Novel Representation for Video-based Person Reidentification with Attribute-constraints

Abstract

Person re-identification is an important task in the field of intelligent video surveillance, which has become one of the research focus spots in the field of computer vision. Video-based person re-identification aims to verify a pedestrian identity of the video sequences which captured from non-overlapping cameras at different time. In this paper, we propose a novel feature extractor based on LSTM networks. These LSTM networks are used to extract the effective space-time feature representation named the attribute-constraints space-time feature (ASTF). Different from other methods, we manually annotate pedestrians in videos with three attributes. In the meantime, the attributes with the IDs of pedestrians are regarded as labels to train the feature extractor. The ASTF representation for a testing video is extracted by this feature extractor, which is an effective space-time feature representation for video-based re-identification. Extensive experiments on two public datasets demonstrate that our approach outperforms the state-of-the-art video-based re-identification methods.