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Mobile Gaze Estimation System for In-Context Market Research
  1. case
  2. Mobile Gaze Estimation System for In-Context Market Research

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Mobile Gaze Estimation System for In-Context Market Research

dac.digital
Advertising & marketing
Information technology
Consumer products & services

Challenges in Hardware-Free Gaze Tracking for Mobile Devices

Current eyetracking solutions require specialized hardware and controlled environments, limiting real-world applicability. Need for accurate gaze estimation across diverse smartphone models, varying lighting conditions, and individual physiological differences without additional equipment.

About the Client

Market research and human experience innovation company specializing in attention measurement technologies

Innovation Goals for Mobile Gaze Estimation

  • Achieve <1cm gaze point detection accuracy across multiple smartphone models
  • Develop auto-calibration system for varying camera positions and screen ratios
  • Enable operation in uncontrolled lighting and user positions
  • Create scalable solution using crowdsourced data for model training

Core System Capabilities

  • Real-time gaze point estimation on mobile screens
  • Cross-device compatibility with auto-calibration
  • Environmental condition adaptation (lighting, angles)
  • Data quality filtering framework for crowdsourced inputs
  • Cloud-based processing and analysis infrastructure

Technology Stack Requirements

Python
PyTorch
OpenCV
MediaPipe
FastAPI
AWS Cloud Services
Docker

System Integration Needs

  • Crowdsourcing platform API (ClickWorker)
  • Mobile device camera SDKs
  • Web application integration framework

Operational Requirements

  • 95% accuracy in gaze estimation under variable conditions
  • Real-time processing latency <100ms
  • Support for 1000+ concurrent users
  • Data privacy compliance (GDPR)
  • Cross-platform compatibility (iOS/Android)

Business Impact of Mobile Gaze Estimation

Enables revolutionary in-context market research capabilities through affordable, accessible hardware-free attention measurement. Reduces research costs by 60-70% while expanding data collection to real-world environments, providing more accurate consumer behavior insights across diverse demographics and device ecosystems.

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