UP Squared Crowd Analytics Toolkit by Sightcorp

UP Squared Crowd Analytics Toolkit by Sightcorp

Aaeon UP Squared Crowd Analytics Kit is a ready-to-use solution with integrated h/w & s/w solution that allows retail shop owners to get anonymous, accurate and actionable insights into their audiences using AI/Deep Learning (DL) algorithms. This kit can also be used to supercharge any digital display to create smart digital signage applications that can analyze the audience standing in front of the screens to target and deliver the content that matches their demographics profiles in real time. No hard coding needed, easy-to-use, fast and hassle-free! The s/w can also be easily integrated with any Content Management System (CMS) and dashboard tools which can be used for dynamic advertising and data visualization. Please Note - Intel is committed to respecting human rights and avoiding complicity in human rights abuses. Intel’s products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.



  • Audience Measurement
    Accurately count people, impressions, viewers and analyze their demographic profiles and measure attention and dwell time
  • Anonymized data & Privacy
    Data is anonymized immediately; our solutions are developed with Privacy by Design and include a Privacy by Default face blurring functionality
  • Content Management Systems (CMS) integration
    Integrate seamlessly with any Content Management System (CMS) for dynamic advertising
  • Data Visualization
    Gather and visualize all audience data in your favourite BI platform for advanced data analysis


WorldwideAI ApplianceRetailFacial Detection/Recognition/ClassificationOn-premise (Private Cloud, Other)Intel® Movidius™ Vision Processing Units (VPU)Intel® Distribution of OpenVINO™ Toolkit powered by oneAPIIntel® Optimization for TensorFlow*Models can be trained - requires labeled dataLinux MobileNetSSDDeep Learning