Summary of Recognizing Identities From Human Skeletons: a Survey on 3d Skeleton Based Person Re-identification, by Haocong Rao et al.
Recognizing Identities From Human Skeletons: A Survey on 3D Skeleton Based Person Re-Identification
by Haocong Rao, Chunyan Miao
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey paper reviews recent advances in person re-identification via 3D skeletons (SRID), a rapidly growing area in pattern recognition. The authors define the SRID task, provide an overview of its origin and major advancements, and formulate a systematic taxonomy that organizes existing methods into three categories based on different skeleton modeling approaches: hand-crafted, sequence-based, and graph-based. They then elaborate on representative models along these categories, analyzing their merits and limitations. The paper also reviews mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding skeleton semantics learning tasks. A thorough evaluation of state-of-the-art SRID methods is conducted over various types of benchmarks and protocols to compare their effectiveness and efficiency. Finally, the authors discuss challenges and promising directions for future research, highlighting the potential applications and research impacts of SRID. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can recognize people from 3D skeleton data. It’s like when you try to find a friend in a crowd by looking at their height, posture, and movements. The authors review many different ways that researchers have tried to do this, grouping them into three categories based on how they model the skeletons. They then discuss which methods are good or bad for certain tasks and show examples of what works well on different datasets. |
Keywords
» Artificial intelligence » Pattern recognition » Self supervised » Semantics » Supervised » Unsupervised
