We Built a Driveway Derby Detector Using the New Ring Appstore APIs

We're going to do something a little different from my usual system design topics. We're turning a Ring camera into a tiny detector for my driveway. My niece and nephew love taking their toy cars for a spin whenever they visit. They race up and down the driveway with lots of enthusiasm. And let's just say their braking skills are still a work in progress. It made me wonder whether I could use a camera to spot anything coming up the driveway at speed, whether it's their toy cars or an actual vehicle. So, I decided to try to build it. It's a new camera that Ring sent me. What makes this camera interesting isn't just a hardware. It's a new Ring app store. Through this new store, there's a developer feature that allows us to subscribe to camera events through a partner API. This essentially turns the camera into an app platform. Third party developers can register for events when something happens like motion, vehicle detection, or package detection. The camera's cloud service send an HTTP request to a back end. For this video, we'll subscribe to vehicle detection events. When a car drive up my driveway, we'll get an event, download a video clip, run a machine learning model to detect the vehicle, calculate it speed, and display everything on the dashboard. Let's get the camera installed. I'm placing it where it has a clear view of my driveway. When a car enters this zone, the camera will send us an event. After on boarding and linking the camera into my account, I can enable the new integration feature. In the portal, we register a web hook URL and select the events we want. For this video, we choose vehicle detection in the driveway zone. Whenever a vehicle enters this zone, the platform senses an event. Before we look at the code, let's walk through the architecture at a high level. The flow is pretty straightforward. The camera detects a vehicle and notifies a backend. A backend downloads the video clip tied to the event, runs a YOLO model to detect the vehicle, estimates the speed based on how far it moves across the frames, and stores the result so our dashboard can show it. That's the entire loop. The camera sees something. The web hook fires. The back end does the heavy lifting and the dashboard shows what happened. Now let's look at the key code paths. The partner API provides an endpoint to register a web hook. Once that's set up, events start arriving at a /weet hook handler. The payload contains an event type, the camera ID, a timestamp, and event ID we can later use to retrieve the video clip. The handler verifies the signature, process the JSON, acknowledges the request, and cues up the work. The heavy processing happens in the background so we can return quickly. Once we have the event, we download the video clips associated with it. The API gives us a temporary link to the MP4 file. The clip usually spans a few second around the detection, which is enough for estimating motion. The speed detection uses a model from the YOLO family. YOLO stands for you only look once. It's a popular real-time object detection model that looks at each frame and tells us where the objects are. We're not going to do anything fancy. We're simply using it to locate the vehicle in each frame so we can track how it moves across the scene. As the vehicle moves along the driveway, it travels a certain number of pixels. With a simple calibration value that maps pixels to meters for this camera, we can turn that pixel movement into a speed estimate. The output gives us a clean number how fast the vehicle was going up the driveway as it passed the camera. The dashboard reads from the back end and display the detection. We see our family's vehicles, their speeds, and the actual clip that triggered the event. When we click on a detection, the dashboard downloads and plays the clip. This ties everything together. What the camera saw, what the model detected, and how fast our vehicles were going. By exposing events as web hooks, this camera becomes a programming platform. While I'm using it today for a fun demo, the real potential here is proactive safety. We could trigger real-time alerts if a car enters the driveway too fast or ensures little ones aren't getting too close to the road. It helps prevent accidents before they happen. And this scales beyond the home. Businesses can use this capability for retail foot traffic analysis, shoplifting detection, or workplace safety alerts like instantly detecting if a forklift is speeding in a warehouse. It effectively turns a camera into an active problem solver. You can request early access to build for the Ring app store at developer. amazon. com/ rain.