Group Re-Identification with Multi-grained Matching and Integration

Abstract

The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem.Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership. In this paper, we propose a novel conceptof group granularity by characterizing a group image by multi-grained objects: individual persons and sub-groups of two andthree people within a group. To achieve robust group Re-ID,we first introduce multi-grained representations which can beextracted via the development of two separate schemes, i.e. onewith hand-crafted descriptors and another with deep neuralnetworks. The proposed representation seeks to characterize bothappearance and spatial relations of multi-grained objects, and isfurther equipped with importance weights which capture varia-tions in intra-group dynamics. Optimal group-wise matching isfacilitated by a multi-order matching process which in turn,dynamically updates the importance weights in iterative fashion.We evaluated on three multi-camera group datasets containingcomplex scenarios and large dynamics, with experimental resultsdemonstrating the effectiveness of our approach. The published dataset can be found in \url{http://min.sjtu.edu.cn/lwydemo/GroupReID.html}

14 pages, 10 figures, to appear in IEEE transaction on Cybernetics

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• FaceNet: A Unified Embedding for Face Recognition and Clustering
• In Defense of the Triplet Loss for Person Re-Identification
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• Deep Ranking for Person Re-identification via Joint Representation Learning
• Learning Correspondence Structures for Person Re-identification
• IAN: The Individual Aggregation Network for Person Search

Cited by in corpus (6)

• Learning Multi-Attention Context Graph for Group-Based Re-Identification
• Part-Guided Attention Learning for Vehicle Instance Retrieval
• Exchangeable deep neural networks for set-to-set matching and learning
• DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning
• Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
• PANDA: A Gigapixel-level Human-centric Video Dataset