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.
