In our latest Tech Talk episode podcast, Perifery’s CTO Jason Perr and AI Sales Manager Nick Kind discuss the advancements in localized object recognition models and their impact across industries. Join us as we breakdown how these models work, why we prioritize on-premise solutions, and how companies can unlock unparalleled efficiencies and insights using AI Plus for object recognition.
Why Localized AI Matters for Your Business
One of the main questions we hear at Perifery is why we emphasize localized, on-premises object recognition models over cloud-based solutions. As Jason explains, our journey began with experimenting on cloud AI platforms, only to find that the costs escalated rapidly. By transitioning to a localized approach, our clients can process huge volumes of data without incurring per-second fees—a game-changer for organizations needing to analyze extensive video or image libraries. With no additional fees tied to usage, this model makes large-scale AI integration accessible and sustainable.
Understanding Object Detection vs. Object Recognition
A common misconception in AI terminology is the interchangeable use of “object detection” and “object recognition.” Traditional object detection models can identify general categories (like “car” or “water bottle”) but struggle to recognize specific details without extensive custom training. Perifery’s model takes it further by adding vision capabilities that provide detailed responses for detected objects. Imagine identifying not just a car but recognizing it as a Lamborghini or Ferrari, all without extensive, costly training. This approach enables businesses to achieve remarkable accuracy right out of the box.
The 3 Levels of Perifery’s Object Recognition Model
To make the most of this technology, we’ve structured our object recognition model with three progressive training levels, each tailored to different user needs:
Level 1: Out-of-the-Box Recognition
No training needed, yet highly accurate. Ideal for organizations needing reliable, rapid object identification without customization.
Level 2: Customizable Training via Roboflow
Perfect for businesses needing advanced recognition for specific products or unique attributes. Using Roboflow’s intuitive platform, clients can add a layer of customization to refine the AI’s ability to detect specific items like brand logos or model variants.
Level 3: Vision Model with Contextual Intelligence
Combining object recognition with a vision model enables detailed scene analysis, such as understanding poker hands at a gaming table. This level creates a fully contextualized understanding of what’s happening in the video, going beyond mere object identification.
Industries Ripe for Transformation
The applications of Perifery’s model are wide-reaching. Consumer product companies can use it to streamline asset retrieval for marketing, searching for specific products across an entire digital content library without manual tagging. For Media and Entertainment (M&E) companies, object recognition facilitates compliance with product placement contracts by accurately tracking product visibility within shows or movies. AI also ensures continuity across episodes, identifying any inconsistencies with props or branding.
Join Us for a Deeper Dive
Whether you’re interested in reducing operational costs, enhancing your content management capabilities, or increasing efficiency across your production pipeline, our podcast offers insights you won’t want to miss. Discover the possibilities of object recognition, directly from the minds shaping this technology.
Listen to the full episode here and explore how AI+ can elevate your business with precision object recognition.
