AI recognition and spatial intelligence for smart-glasses outdoor advertising - ON-1244
Project type: ResearchDesired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences, Interactive arts and technology, Social Sciences & Humanities
Company: Darabase Canada Limited
Project Length: 6 months to 1 year
Preferred start date: 11/02/2026
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 1
Desired education level: Master'sPhD
Open to applicants registered at an institution outside of Canada: No
About the company:
Darabase Canada Limited is a Toronto-based technology company building commercial infrastructure for AI-powered augmented reality advertising and spatial media.
As AI-enabled smart glasses bring digital content into the physical world, Darabase enables real-world locations to become rights-cleared digital media inventory by managing digital rights, permissions, inventory and commercial transactions.
Darabase is currently exploring an AI-powered smart-glasses commercialization initiative with a leading Canadian outdoor media company, focused on how existing outdoor advertising could be enhanced through AI-powered recognition and immersive spatial experiences. The precise pilot scope and implementation approach are still being refined.
The initiative builds on existing outdoor advertising infrastructure rather than requiring new physical media formats. A consumer wearing smart glasses could look at an existing advertisement, have the advertising asset and campaign creative recognized automatically, and access a contextual digital experience without QR codes, markers or other physical triggers.
Darabase is seeking applied research partners to advance the AI, computer-vision and spatial-intelligence capabilities required to scale this concept across multiple advertising assets, campaigns and physical locations.
Describe the project.:
Darabase is seeking to research and validate AI and computer-vision approaches that enable smart glasses to automatically recognize outdoor advertising assets and the campaign creative displayed within them, understand the surrounding spatial and location context, and associate the recognized advertisement with the correct digital experience.
An initial Proof of Concept has been scoped to explore image tracking against a known campaign creative. The proposed research would investigate how this approach could evolve into a scalable system capable of supporting multiple campaigns, advertising formats and physical locations.
Research areas may include visual recognition and matching of advertising creative; object and asset recognition; visual localization; combining visual signals with geolocation and media-inventory metadata; recognition under varying lighting, viewing angles, distances and occlusion; reducing false-positive activations; and efficient inference suitable for resource-constrained wearable devices.
The project would also investigate how recognized physical advertisements can be associated with the correct contextual and spatial digital experience while preserving existing advertiser workflows.
The intended outcome is a validated technical approach and prototype demonstrating how AI-enabled smart glasses could reliably recognize approved outdoor media and activate the appropriate AR experience at scale.
The research is intended to support commercialization opportunities with outdoor media partners, including an initiative currently being explored with a leading Canadian outdoor media company, focused on how existing outdoor advertising infrastructure could evolve into intelligent, interactive and measurable digital media.
Required expertise/skills:
The ideal candidate will have strong experience in computer vision, machine learning and image recognition, with an interest in spatial computing and real-world AI applications.
Relevant expertise may include:
• Computer vision and deep learning
• Image recognition, feature matching and similarity search
• Object detection and visual localization
• Multimodal AI
• Spatial computing and augmented reality
• Python
• PyTorch and/or TensorFlow
• OpenCV or related computer-vision frameworks
• Geospatial data and metadata integration
• Edge/mobile AI optimization
• Experimental design and evaluation of recognition accuracy, latency and false positives
Experience with smart glasses, wearable computing, AR/XR development or resource-constrained edge devices would be an asset.
The candidate should be comfortable developing and testing AI systems under variable real-world conditions, including changes in viewing angle, distance, lighting, occlusion and physical environment.
