From Footfall to Behavior: Tracking Behavior Without Tracking People inside Shopping Malls

From Footfall to Behavior: Tracking Behavior Without Tracking People inside Shopping Malls

From Efficiency in CRE by Mateo Chiyangi

January 12, 2026 · 38 min

About this episode

The episode discusses the challenges and considerations of using computer vision AI to understand behavior in physical spaces without compromising privacy.

In this episode of the podcast, I sit down with Galvin Widjaja, CEO of Lauretta, to unpack what it really means for computer vision AI to “understand” the physical world. Moving beyond hype around models and automation, the conversation explores why most computer vision systems fail in real environments touching on dataset limitations, time and identity problems, and the difference between counting people and understanding behavior. Drawing on real-world deployments with organizations such as Lendlease (shopping mall), Changi Airport, the Transportation Security Administration (TSA), and the U.S. Department of Homeland Security (DHS), Galvin explains why effective physical AI isn’t about giving machines human-like intelligence, but about designing systems that detect patterns, respect privacy, show anomalies, and leave judgment and action to humans who have skin in the game.

People in this episode

Host: Mateo Chiyangi

Guest: Galvin Widjaja

Topics covered

  • computer vision
  • AI
  • behavior tracking
  • privacy
  • real-world applications
  • data limitations

Keywords

  • computer vision
  • AI
  • behavior tracking
  • privacy
  • real-world deployments
  • data limitations
  • pattern detection

Mentioned in this episode

Organizations: Lauretta, Lendlease, Changi Airport, Transportation Security Administration, U.S. Department of Homeland Security

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