Recognizing Identities From Human Skeletons: A Survey on 3D Skeleton Based Person Re-Identification

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 survey, 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 based on different skeleton modeling ($i.e.,$ hand-crafted, sequence-based, graph-based). Then, we elaborate on the representative models along these three categories with an analysis of their merits and limitations. Meanwhile, we provide an in-depth review of 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 further conducted over various types of benchmarks and protocols to compare their effectiveness and efficiency. Finally, we discuss the challenges of existing studies along with promising directions for future research, highlighting research impacts and potential applications of SRID.
Comment: A curated collection of valuable resources (papers, codes, data, etc.) is available at https://github.com/Kali-Hac/SRID