mlsanityData to 14 Oct 2026

Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology

arXiv 2506.04143 ↗First posted 4 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets. Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, fine grained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration. This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS). The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation. We experimented on the well-known Market1501 dataset. The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 4 Jun 2025.

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21 of 43 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  4. 2017Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)cited by 473
  5. 2018Learning Discriminative Features with Multiple Granularities for Person Re-Identificationcited by 291
  6. 2016Person Re-identification: Past, Present and Futurecited by 181
  7. 2018Human Semantic Parsing for Person Re-identificationcited by 100
  8. 2017Improving Person Re-identification by Attribute and Identity Learningcited by 96
  9. 2017Pose-driven Deep Convolutional Model for Person Re-identificationcited by 86
  10. 2017AlignedReID: Surpassing Human-Level Performance in Person Re-Identificationcited by 61
  11. 2017Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identificationcited by 52
  12. 2016End-to-End Comparative Attention Networks for Person Re-identificationcited by 42
9 more
  1. 2016Deep Attributes Driven Multi-Camera Person Re-identificationcited by 21
  2. 2017Deep-Person: Learning Discriminative Deep Features for Person Re-Identificationcited by 16
  3. 2018Attention Driven Person Re-identificationcited by 11
  4. 2015Deep Ranking for Person Re-identification via Joint Representation Learningcited by 10
  5. 2018Discriminative Feature Learning with Foreground Attention for Person Re-Identificationcited by 9
  6. 2016An Enhanced Deep Feature Representation for Person Re-identificationcited by 6
  7. 2015Constrained Deep Metric Learning for Person Re-identificationcited by 2
  8. 2017Person Re-identification Using Visual Attentioncited by 2
  9. 2018Pyramid Person Matching Network for Person Re-identificationcited by 1

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