Data Collection

Data collection and video annotation: weapon detection on the streets

Image

The system enabled a 99% accuracy in detecting weapons on people in both street and indoor environments.

Industry Video Systems and Video Analysis
Timeline 28 days
Data 100 hours of video for annotation
Image
Industry Video Systems and Video Analysis
Timeline 28 days
Data 100 hours of video for annotation

The Challenge

Our client, a company specializing in video surveillance and video analysis, approached us with a request to develop a dataset for a weapon detection system designed to identify weapons carried by individuals on the streets of a large city. The system needed to work effectively in real-world urban environments, both outdoors and indoors.

However, the task presented several challenges:

  • Lack of available datasets: There were no existing datasets with weapon images in such specific urban settings, which made it difficult to create the necessary training data.
  • Insufficient data for model training: Parsing open-source video material didn’t provide enough footage to build a reliable model.
  • Uncertainty about data collection: The client was unsure how to organize the process of collecting a diverse and high-quality dataset.

To solve these challenges, the client turned to us for help.

Our Solution:

We devised a detailed and scalable methodology for collecting and annotating data that ensured the creation of a high-quality dataset to train the client’s weapon detection system.

  • 01

    Data Collection Strategy

    Utilizing Extras and Prop Weapons:

    • To create realistic data, we coordinated several groups of extras, each equipped with prop weapons of various sizes and types, ranging from small pistols to larger models.
    • These weapons were selected to closely resemble real firearms, ensuring the scenes captured were realistic and suitable for weapon detection.

    Planning Routes for Extras:

    • We mapped out specific routes for the extras to follow through key urban locations with existing surveillance cameras. The goal was to ensure frequent exposure to the camera frames while simulating real-world street conditions.
    • The routes were strategically chosen to maximize the chances of capturing realistic weapon-carrying scenarios in common urban settings.

    Variety of Scenes:

    • Each extra was filmed in 10 different scenarios: 5 outdoors and 5 indoors. This variety created diverse situations, ranging from walking calmly to more dynamic actions, such as turning corners or entering buildings, offering a wide range of footage for training the model.
  • 02

    Video Collection and Processing

    Filming and Gathering Data:

    • The videos were collected from both existing surveillance cameras and newly installed cameras designed to capture specific angles and movements.
    • We ensured that the footage was filmed under different lighting conditions and varied weather, contributing to the diversity of the dataset.

    Real-World Conditions:

    • Filming was done in both daytime and nighttime settings, allowing the model to account for different environmental factors and lighting conditions that could impact weapon detection accuracy.
  • 03

    Video Annotation and Bounding Box Detection

    Manual Annotation:

    • After collecting the footage, the video was passed on to our team of skilled annotators, who meticulously labeled the weapons in each frame with bounding boxes. We ensured that this process was detailed and precise, with uniformity across all data points.

    Quality Control:

    • Every annotated frame underwent a two-step verification process to guarantee the accuracy and consistency of the labels. This system minimized the risk of errors, ensuring that the final dataset was of the highest quality.
  • 04

    Delivery and Support

    Dataset Delivery:

    • Upon completion of the annotation and verification process, we delivered the client a fully annotated dataset, with accurate bounding boxes on each frame. This dataset was ready for use in training the client’s weapon detection model.

Results:

  • With 99% weapon detection accuracy, the client enhanced the efficiency and reliability of their video surveillance operations.

  • Efficient and on-schedule delivery: the complete data collection and annotation process was finalized within 28 days, aligning with the client’s demanding timeline.

  • Documentation and future support: We thoroughly documented the entire process, enabling the client to replicate similar projects in the future with ease.

Similar Cases

  • Image
    Data Collection

    Optimizing Waste Collection: Data Gathering for City Administration

    How can AI improve waste collection efficiency? We helped the city administration build a high-quality dataset that boosted waste bin […]

    Lean more
  • Image

    Grouping Listings into Product Cards

    Thousands of listings. Different sellers. Endless naming variations. Helping buyers navigate this chaos was the challenge facing one of the […]

    Lean more
  • Image

    Response Suggestion for an Online Classifieds Platform

    Predicting the right reply isn’t just about words — it’s about tone, context, and timing. Our annotation work made AI […]

    Lean more
  • Image
    Image Annotation

    Semantic Segmentation for Interior Design: A Complex Multiclass Annotation Project

    How do you segment every single object in a cluttered interior photo — 30+ classes per image? We designed a multi-step annotation pipeline to handle complexity without losing precision.

    Lean more
  • Image
    Audio Labeling services for ml Audio Transcription

    Banking Call Categorization

    To automate call categorization, one of Eastern Europe’s largest banks entrusted us with sensitive voice data covering credit, debit, deposits, and balances. We built a privacy-first annotation pipeline with in-house experts, multilayer validation, and weekly reporting to ensure both compliance and accuracy—enabling faster, smarter service automation.

    Lean more

Ready to get started?

Tell us what you need — we’ll reply within 24h with a free estimate

    What service are you looking for? *
    What service are you looking for?
    Data Labeling
    Data Collection
    Ready-made Datasets
    Human Moderation
    Medicine
    Other (please describe below)
    What's your budget range? *
    What's your budget range?
    < $1,000
    $1,000 – $5,000
    $5,000 – $10,000
    $10,000 – $50,000
    $50,000+
    Not sure yet
    Where did you hear about Unidata? *
    Where did you hear about Unidata?
    Head of Client Success
    Andrew
    Head of Client Success

    — I'll guide you through every step, from your first
    message to full project delivery

    Thank you for your
    message

    It has been successfully sent!

    This website uses cookies to enhance your experience, analyze traffic, and deliver personalized content and ads. By clicking "Accept", you consent to the use of cookies, as described in our Cookie Policy. Please choose your cookie preference.