Image Annotation

Pose Estimation for Proctoring

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How do you teach AI to recognize when a student is cheating during an exam? By accurately annotating 6000 images of real exam scenarios — and that’s exactly what we did.

Industry Education
Timeline 3 months
Data 6000 images
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Industry Education
Timeline 3 months
Data 6000 images

Task

An education technology company approached us with a request to annotate a dataset of human poses captured during exam sessions. The goal was to train machine learning models that could automatically detect students’ body positions and movement patterns in real time, helping identify suspicious behavior such as looking away from the screen, leaning towards neighbors, or leaving the frame.

The main objectives were:

  • Accurate annotation of 6000 images with human pose keypoints.
  • Model training support: provide training data for ML algorithms to recognize poses in various scenarios — different angles, lighting, occlusions, and group settings.
  • Short turnaround time to align with the client’s internal development cycle.

Solution

  • 01

    Iterative Annotation Workflow

    We structured the project into three iterative batches of 2000 images. The first batch was manually annotated by our expert team to establish baseline quality and provide the client with a reference dataset.

    The client then used this baseline to apply pre-annotation via their internal tools for the next batches, which we reviewed, corrected, and finalized. This reduced annotation time by up to 40% in subsequent stages and ensured consistency throughout the dataset.

  • 02

    Handling Complex Poses in Crowded Settings

    Many images featured multiple individuals in a single frame, often seated close together with overlapping limbs and furniture. We developed strict internal guidelines to handle:

    • Body part occlusion
    • Crossed limbs and overlapping keypoints

    Variations in posture (e.g., slouched, turned, or partially out of frame)
    This ensured high granularity and precision in point placement, which was essential for downstream model training.

  • 03

    Team Training and Domain Immersion

    To meet the project’s high accuracy requirements, our annotation team underwent specialized training, including:

    • Studying anatomical diagrams and pose modeling references
    • Watching client-provided exam footage for context
    • Aligning on edge cases via weekly QA review sessions

    This preparation enabled the team to better identify and annotate subtleties in human posture and movement.

Results

  • 6000 images annotated within 3 months, including verification and correction cycles

  • Each batch of 2000 images delivered on time, supporting the client’s agile development timeline

  • The high-quality dataset led to a measurable improvement in the client’s pose detection model accuracy, contributing to more effective and automated exam monitoring

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