A Survey on Cross-Modality Heterogeneous Person Re-identification [跨模态异构行人再识别的研究进展]

作者：Sun R.^*; Zhao Z.; Yang Z.; Gao J.

来源：Pattem Recognition and Aitificial Intelligence, 2020, 33(12): 1066-1082.

DOI：10.16451/j.cnki.issn1003-6059.202012002

• Cross-Modality
• Deep Learning
• Feature Representation
• Heterogeneous Person Re-identification(Hetero-ReID)
• Metric Learning
• Person Re-identification(ReID)

Homogeneous person re-identification(ReID) based on RGB images is well researched. With the development of public security and pedestrian retrieval in complex real-world situations, the field of pedestrian matching based on multi-modal heterogeneous data sources, called cross-modality heterogeneous ReID, is explored. In this paper, heterogeneous ReID and the differences from homogeneous ReID are firstly summarized. Then, heterogeneous ReID is described under six types of scenarios, including text-to-image, images-to-video, cross-resolution, infrared-visible, RGB-depth and sketch-photo. The relevant popular datasets, representative algorithms and excellent performance of these algorithms on datasets are summarized and reorganized. Finally, the current research progress and the future research trends of cross-modality heterogeneous person re-identification are discussed.

• 单位
  合肥工业大学