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A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects

arXiv 2401.15296 ↗First posted 27 Jan 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origin and major advancements. Secondly, we formulate a systematic taxonomy that organizes existing methods into three categories centered on hand-crafted, sequence-based, and graph-based modeling. Then, we elaborate on the representative models along these three types with an illustration of foundational mechanisms. Meanwhile, we provide an overview of mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding common methods. A thorough evaluation of state-of-the-art SRID methods is further conducted over various types of benchmarks and protocols to compare their effectiveness, efficiency, and key properties. Finally, we present the key challenges and prospects to advance future research, and highlight interdisciplinary applications of SRID with a case study.

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27 of 67 references.

References · 27

  1. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  2. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  4. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  5. 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 786
  6. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  7. 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 549
  8. 2017Person Transfer GAN to Bridge Domain Gap for Person Re-Identificationcited by 480
  9. 2021Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognitioncited by 322
  10. 2020Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Predictioncited by 132
  11. 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 113
  12. 2022PoseGPT: Quantization-based 3D Human Motion Generation and Forecastingcited by 78
15 more
  1. 2023SkeletonGait: Gait Recognition Using Skeleton Mapscited by 41
  2. 2023GPGait: Generalized Pose-based Gait Recognitioncited by 40
  3. 2022Spatial Transformer Network on Skeleton-based Gait Recognitioncited by 33
  4. 2016Space-Time Representation of People Based on 3D Skeletal Data: A Reviewcited by 24
  5. 2020A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identificationcited by 20
  6. 2016Recurrent Attention Models for Depth-Based Person Identificationcited by 18
  7. 2017Skeleton-aided Articulated Motion Generationcited by 14
  8. 2023TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identificationcited by 14
  9. 2020Brain-inspired Search Engine Assistant based on Knowledge Graphcited by 13
  10. 2020Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identificationcited by 12
  11. 2023Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identificationcited by 9
  12. 2021SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identificationcited by 8
  13. 2021Keypoint Message Passing for Video-based Person Re-Identificationcited by 8
  14. 2021Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identificationcited by 7
  15. 2022SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identificationcited by 6

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