Parcel Videos Dataset

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Project Overview:

Objective

The “Parcel Videos Dataset” project was undertaken to curate a comprehensive dataset aimed at facilitating the training of machine learning models for parcel recognition and tracking. This dataset serves as a valuable resource for developers and researchers in the field of logistics and delivery services.

Scope

The scope of this project encompassed the collection of video footage depicting parcels in various real-world scenarios, along with the meticulous annotation of these videos to identify key attributes such as parcel size, type, condition, and tracking information.

Sources

Surveillance Cameras: Collecting footage from surveillance cameras placed in warehouses, delivery vehicles, and postal facilities.
User Submissions: Encouraging users and parcel delivery professionals to contribute their video recordings.
Public Datasets: Accessing publicly available video datasets containing parcel-related content.

Data Collection Metrics

Total Videos Collected: 20,000 videos
User Submissions: 15,000 videos
Surveillance Cameras: 3,000 videos
Public Datasets: 2,000 videos

Annotation Process

Stages

Parcel Recognition: Annotating each video to identify parcels, specifying attributes such as size, type (e.g., package, envelope), and condition.
Tracking Information: Logging metadata related to parcel tracking, including timestamps, locations, and tracking numbers.

Annotation Metrics

Videos with Parcel Annotations: 20,000
Parcel Tracking Metadata Logged: 18,000

Quality Assurance

Stages

Annotation Verification: Our team of experts reviewed and verified parcel annotations to guarantee accuracy.
Data Quality Control: We conducted data cleansing to remove low-quality or irrelevant video clips, ensuring that the dataset only contains high-value content.
Data Security: Sensitive parcel data was handled with utmost care, adhering to privacy regulations and obtaining user consent where necessary.

QA Metrics

Annotation Validation Cases: Approximately 3,000 annotations reviewed to ensure the accurate identification of parcel attributes across diverse scenarios.
Data Cleansing: A significant effort was dedicated to purging low-quality videos, with an emphasis on removing clips that failed to meet the project’s visual clarity and relevance standards.
Security Compliance Checks: Conducted 500 rigorous tests to verify adherence to data protection laws, focusing on secure handling and storage of sensitive tracking information.

Conclusion

The “Parcel Videos Dataset” stands as a testament to our commitment to providing high-quality data resources. With a substantial volume of annotated videos and comprehensive tracking information, this dataset empowers the development of advanced machine learning models for parcel recognition, tracking, and logistics optimization. It caters to a wide range of applications in the logistics and delivery industry, offering a reliable means to enhance parcel management, optimize delivery routes, and improve customer experiences.

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