Situational diversity in video person re-identification: introducing MSA-BUPT dataset

Abstract

Abstract Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a limited range of real-world scenarios, and they have several obvious limitations: limited camera viewing angles, tiny variations of the shooting scene, and even errors in manual labels. These disadvantages prevent video person ReID from being widely used in real-life scenarios. In this work, a new high-quality multi-situation video person ReID dataset, named MSA-BUPT, is built to promote the video person ReID task in large-scale urban surveillance. Specifically, MSA-BUPT contains 684 identities, 2,665 trajectories, and nearly 250,000 frames from 200-h videos across various complex scenarios. Person attribute annotations and unannotated video data are also provided for other research perspectives, such as cross-modality ReID, cross-domain ReID, and so on. Furthermore, two plug-and-play components are used to improve retrieval capabilities: a new scenario-based data augmentation method is proposed to alleviate the person misalignment problem; a re-ranking strategy based on person attribute is applied to make secondary adjustments to the content to the results of the model. The extensive experimental results show that the above methods improve the performance of some representative state-of-the-art models on the new dataset.

Keywords

• New video person re-identification dataset
• Scenario-based data augmentation
• Attribute-based re-ranking strategy
• Deep learning
• Urban surveillance