Parcel Videos Data Collection

Project Overview:

Objective

We aim to compile a diverse dataset of parcel and package handling videos through our Parcel Videos Data Collection initiative. This collection aims to enhance AI systems responsible for automating logistics, ensuring the proper handling of packages, and developing intelligent surveillance for warehouses and shipping centers.

Scope

Transitioning to packaging materials, we begin with standard cardboard boxes. These are versatile and widely used for a variety of products. Moving forward, we see padded envelopes, ideal for fragile or smaller items requiring additional protection. Then, bubble wrap appears, providing cushioning for breakable goods. Additionally, plastic mailers come into view, offering a lightweight and waterproof option for shipping.

Parcel Videos Data Collection
Parcel Videos Data Collection
Parcel Videos Data Collection
Parcel Videos Data Collection

Sources

  • Warehouses and Sorting Centers: Collaborate with logistics companies to capture videos of parcels as they move through conveyor systems, get sorted, and loaded onto vehicles.
  • Delivery Process: Record videos of delivery personnel handling and delivering parcels to various locations: homes, businesses, drop-off points, etc.
  • Return and Damaged Goods: Document the process of dealing with returned goods and assessing parcel damages.
  • User-generated Content: Encourage customers or receivers to share videos of unboxing or any observed mishandling.
Parcel Videos Data Collection
Parcel Videos Data Collection

Data Collection Metrics

  • Total Data Points: 40,000 videos
  • Warehouse Footage: 20,000
  • Delivery Footage: 10,000
  • Return/Damage Assessment: 5,000
  • User-generated Videos: 5,000

Annotation Process

Stages

  1. Parcel Type: Label the parcel’s contents if identifiable (e.g., electronics, perishables, fragile items).
  2. Handling Annotation: Document the observed handling method. For instance, indicate whether it was handled manually or automatically, and note if it was done with or without care.
  3. Environment & Context Tagging:

    Indicate the primary setting, such as a warehouse, outdoor area, or residential zone. Additionally, specify any relevant context, like whether it was raining, crowded, or if the parcel was on a conveyor belt.

     

Annotation Metrics

  • Parcel Type Annotations: 40,000
  • Handling Annotations: 40,000
  • Environment Tags: 40,000
Parcel Videos Data Collection
Parcel Videos Data Collection
Parcel Videos Data Collection
Parcel Videos Data Collection

Quality Assurance

Stages

Video Quality Review: Check that each video meets a defined standard of clarity, stability, and resolution.
Privacy Assurance: Ensure no sensitive information or identifiable personal data is visible in the videos, especially in user-generated content.
Metadata Verification: Regular audits to guarantee the accuracy and consistency of annotations.

QA Metrics

  • Videos Needing Quality Adjustments: 4,000 (10% of total)
  • Privacy Review on All Videos: 40,000 (100% due to potential sensitivity)
  • Metadata Audit Samples: 8,000 (20% random sampling)

Conclusion

The Parcel Videos Data Collection Initiative is poised to revolutionize the logistics and e-commerce industries. With a holistic view of the parcel lifecycle and detailed annotations, AI systems can be better trained for automation, surveillance, and quality assurance, ensuring that each package reaches its destination safely and efficiently.(Parcel Videos Data Collection)

quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

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

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

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

Let's Discuss your Data collection Requirement With Us

To get a detailed estimation of requirements please reach us.

Scroll to Top