Large-scene Multi-camera Person Re-identification dataset

Project Overview:

Objective

To create a robust dataset tailored for person re-identification across large scenes using multi-camera setups. This dataset will be instrumental in advancements for security and surveillance systems, smart cities, and crowd management technologies.

Scope

Acquire visuals from sprawling areas using numerous camera angles and varying resolutions. Ensure the capturing of diverse scenarios, lighting conditions, and crowd densities to facilitate precise person re-identification.

Large-scene Multi-camera Person Re-identification dataset
Large-scene Multi-camera Person Re-identification dataset
Height and Weight Image Dataset
Large-scene Multi-camera Person Re-identification dataset

Sources

  • Large public spaces: airports, train stations, shopping malls, and parks.
  • Events: concerts, sports events, and conventions.
  • Different camera brands and resolutions for diverse image qualities.
  • Daytime and nighttime captures.
  • Collaboration with security firms for accessing pre-recorded surveillance footage (ensuring privacy and permissions).
Height and Weight Image Dataset
Large-scene Multi-camera Person Re-identification dataset

Data Collection Metrics

  • Total Data Points: 1,200,000 frames
  • Public Spaces: 400,000 frames
  • Events: 400,000 frames
  • Nighttime Captures: 200,000 frames
  • Diverse Camera Sources: 200,000 frames

Annotation Process

Stages

  1. Raw Data Refinement: Filtering frames lacking clarity or with obstructions.
  2. Person Identification: Tagging and tracking individual persons across frames and camera feeds.
  3. Cross-camera Linking: Associating the same individual’s appearances from different camera angles.
  4. Accessory Annotations: Noting unique identifiers like bags, hats, or specific clothing to assist re-identification.
  5. Quality Inspection: Meticulous checks to ascertain precise person tagging and linking.

Annotation Metrics

  • Individual Persons Tagged: 2,000,000 instances (some individuals appear in multiple frames)
  • Cross-camera Annotations: 500,000 links made
  • Accessory Annotations: 800,000 instances
  • Annotations Reviewed: 240,000 (20% of total for quality assurance)
Large-scene Multi-camera Person Re-identification dataset
Large-scene Multi-camera Person Re-identification dataset
Large-scene Multi-camera Person Re-identification dataset
Large-scene Multi-camera Person Re-identification dataset

Quality Assurance

Stages

Expert Consultation: Surveillance and security specialists scrutinized annotations for real-world relevance and accuracy.
Automated Consistency Verifications: Software cross-referenced linked annotations for discrepancies.
Inter-annotator Reliability Checks: Collaborative reviews among multiple annotators on shared datasets bolstered annotation consistency.

QA Metrics

  • Annotations Verified by Experts: 240,000 (20% of total annotations)
  • Identified and Corrected Mismatches: 24,000 (2% of total annotations)

Conclusion

The Large-scene Multi-camera Person Re-identification Dataset is a landmark compilation, tailored for sophisticated surveillance, crowd management, and smart city integrations. Through its comprehensive coverage and detailed annotations, it promises unprecedented accuracy in person re-identification, heralding a new era for security and urban management solutions.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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