Troubleshooting Verkada's Pet Detection Feature
Verkada's AI-powered analytics, including pet detection, are designed to make your search for specific events effortless. The ability to filter footage to see only clips containing your cat or dog can be incredibly useful. However, like any AI system, its accuracy can be affected by various factors, leading to missed events or false positives.
If you're experiencing issues with the pet detection feature, this guide will help you understand how it works and what you can do to optimise its performance for more reliable results.
Common Pet Detection Issues
You might be facing one of the following problems:
- The system fails to detect your pet when it is clearly in the frame.
- You receive frequent false alerts, with the system identifying shadows, objects, or other animals as pets.
- Pet detection works intermittently, catching some events but not others.
- The feature is not available or appears greyed out in your Verkada Command settings.
- The system misclassifies your dog as a cat, or vice versa.
- You are trying to detect animals other than cats or dogs with no success.
Step-by-Step Guide to Improve Pet Detection Accuracy
Follow these steps to fine-tune your setup and get the most out of this feature.
1. Ensure Pet Detection is Enabled Correctly
First, you need to verify that the feature is properly configured in your Verkada Command dashboard.
- Log into your Verkada Command account.
- Navigate to the Devices page and select the camera you want to configure.
- Go to the camera's Settings tab.
- Under the Analytics section, ensure that People Detection is enabled. Pet detection is a component of the broader object detection feature and requires People Detection to be active.
- Once confirmed, go to the main Analytics tab for the camera and ensure the Pet Detection filter is enabled there.
2. Optimise Camera Placement and Angle
The performance of any video analytic is highly dependent on the camera's field of view.
- Ideal Height and Angle: For best results, mount the camera at a height of 8-15 feet (2.5-4.5 metres) and angle it downwards. This top-down perspective provides the AI with a clearer view of the animal's shape and movement, reducing the chance of misidentification.
- Unobstructed View: Make sure the camera's view is not partially blocked by furniture, plants, or other objects. The more of the scene the camera can see, the better the AI can analyse it.
- Good Lighting: While Verkada cameras have excellent low-light performance, analytics work best in well-lit conditions. Ensure the area is adequately illuminated to avoid deep shadows that can be misinterpreted as objects.
3. Understand the Feature's Scope
It's important to have the right expectations for the technology.
- Designed for Cats and Dogs: Verkada's pet detection model is specifically trained to recognise the unique shapes and movements of cats and dogs. It is not designed to identify other animals like foxes, squirrels, birds, or smaller pets.
- Size and Distance: The animal needs to occupy a sufficient portion of the frame to be detected reliably. A pet that is very far away or only briefly visible at the edge of the screen may be missed.
4. Use Motion Zones to Reduce False Positives
If you are getting alerts for things that are not pets, such as wind-blown debris or moving shadows, you can use motion detection zones to focus the camera's attention.
- In the camera's settings, you can define specific motion detection zones.
- Draw zones around the areas where you expect to see your pet (e.g., the living room floor, a garden path) and instruct the camera to only analyse motion within these areas. This prevents irrelevant movement in other parts of the scene from triggering false events.
By checking your settings, optimising camera placement, and understanding the feature's capabilities, you can significantly improve the accuracy of Verkada's pet detection and turn it into a powerful tool for monitoring your furry friends.