On the December 10, 2025 episode of WebRTC Live, host Arin Sime welcomed Chris Allen, CEO of Red5, to explore how LLMs can be integrated into real-time video workflows to detect critical conditions within live streams for use cases like traffic monitoring and crowd congestion to forest fire detection, firearms recognition, and content moderation.
Chris walks through the technical architecture, focusing on how Red5 Pro’s Brew API enables raw video frame extraction for analysis. He discusses different approaches for these systems, including where WebRTC, RTSP, and MOQ fit in the pipeline, along with practical deployment considerations.
They also look at real-world use case implementations and the evolving role of AI in real-time video systems for safety, automation, and platform integrity.
The episode also features Arin Sime and Tsahi Level-Levi’s Monthly WebRTC Industry Chat. This month, they discussed how to architect a WebRTC solution that scales a group call into a large-audience livestream, covering the main design patterns and considerations for these applications. Watch WebRTC Architecture for Group-to-Livestream Scenarios.
Watch Episode 108: Using AI for Object Detection in Real-Time Video
Episode highlights and key insights below.
Key Insights
⚡ AI object detection opens a new world for real-time video. AI in object detection isn’t just watching video, it’s understanding it. By using visual language models, live streams become sources of real-time intelligence that can trigger alerts, generate explanations, and drive action within seconds. Chris says, “Primarily, people have been looking at WebRTC streaming and using AI to basically create chatbots or AI bots or whatever you want to call it. What we’ve been playing a lot with is a little bit different than that, and that is using a VLM or a visual language model to actually go in and detect what’s happening in the stream.”
⚡ WebRTC isn’t always required, but it shines when latency matters. When it comes to AI-powered analysis of live video, the key is choosing the right protocol for the use case. Chris explains, “I think it’s a good option where it makes sense. […] Usually, it’s the playback in real time in a browser where the WebRTC stuff really matters. That said, MoQ is fast approaching. We’re heavily involved in the development of MoQ as well, the Media over QUIC kind of stuff, and that may very well be the better replacement for these type of use cases. But in the meantime, WebRTC is fantastic for it. If you’re trying to detect things in a video chat application or something like what we’re using with StreamYard right now, then WebRTC is perfect. That’s exactly what you would want to use as the ingest for that detection.”
⚡ Edge AI is promising. As AI continues to expand beyond the cloud, edge devices are starting to show real potential. Chris explains the opportunities and the current limitations of running AI on devices closer to the source: “I think there is going to be real opportunities with Edge, especially with some of the stuff that Apple is doing right now with their image detection on the device. And so mobile phones, I think, are going to be able to do it. I just don’t think you would get it in something like a traffic camera, a security camera, or something. You’re just not going to have that level of power on it. Same with probably with drones and other devices. […] So I don’t want to rule it out. I’m just saying for some of these use cases, it’s not practical today.”
Episode Highlights
The impact of AI object detection use cases
AI object detection is a game-changer for industries that need fast and reliable insights. Chris explains, “The use cases are kind of broad. You’ve got very serious use cases that kind of happen in surveillance and monitoring, smart cities, traffic monitoring, and things like looking for wildfires in a live stream and detecting that super early and not relying on human beings looking at giant grids of video to be able to see that, because you can miss it pretty easily in those situations. So AI does a particularly good job at that sort of thing. Also, using it in the broadcast industry to be able to detect bad things in the stream, like somebody flipping the bird or nudity or all kinds of bad stuff that you might not want in the live event, and it can automatically do that.”
How AI outpaces humans in real-time video monitoring
In high-stakes situations, every second counts. AI-powered object detection can spot potential hazards long before humans can, giving teams precious time to respond. As Chris demonstrates: “Already the model is seeing stuff at 16% likelihood, and depending on the thresholds you wanted to set for alerting, you could have even done it faster, but we’re a minute, 14 seconds into this thing, and it’s alerting now. If I turn on the audio, it’s kind of annoying, but it’ll say, ‘Possible incident on I-575.’ And now it’s highly likely that there’s an issue, and now the fire stuff is actually going up if you can see that. So now it’s pretty darn sure it’s a fire at this point, and it’s alerting accordingly. So now imagine if you could actually send emergency response out to this thing at a minute 30 after this fire started, you could start to put this stuff out pretty quickly.”
Cost and latency are the real bottlenecks.
In real-time AI video, the hardest problem isn’t detection accuracy; it’s making it fast and affordable at scale. As Chris explains, “I think the cost is really the tricky part because especially if you’re using GPUs in the cloud, and they can be insanely expensive. And then also just availability of those things, the GPUs are kind of a commodity, just because you have all kinds of AI work that people are doing. That’s probably one of the more tricky things, which I think it was great you brought up the edge computing thing, because I think that is one of the ways to mitigate this for certain use cases.”
Up Next! WebRTC Live #109
Agentic Workflows That Work in Production
with Alberto González and Mariana López, CTO and COO of WebRTC.ventures and AgilityFeat
Wednesday, January 14, 2026 at 12:30 pm Eastern
