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Now available—the Embedded Vision Summit On-Demand Edition! Gain valuable computer vision and edge AI insights and know-how from the experts at the 2021 Summit.

Kit Thambiratnam, General Manager of the Seedland AI Center, presents the “Multi-modal Re-identification: IOT + Computer Vision for Residential Community Tracking” tutorial at the September 2020 Embedded Vision Summit.

The recent COVID-19 outbreak necessitated monitoring in communities such as tracking of quarantined residents and tracking of close-contact interactions with sick individuals. High-density communities also have many non-resident visitors (delivery, repair, social) and tracking allows safeguarding of sensitive areas like playgrounds. Vision techniques for face recognition and person tracking are severely challenged in real residential communities; for example, poor accuracy for children and the elderly due to sparse training data and suboptimal positioning of cameras. Also, attributes such as whether somebody is sick, or is a visitor, cannot be observed by a camera.

In this talk, Thambiratnam introduces how Seedland builds safer and healthier residential communities in China using cross-modal IOT-plus-vision person tracking and intelligence. He describe his company’s solution, which propagates intelligence from smart community devices and services across camera-based person-tracking to build a rich annotated graph of behavior and attributes. Thambiratnam details technical solutions to real-world challenges in person identification, annotation and multi-day tracking.

See here for a PDF of the slides.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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