How to Fix Inaccurate Uniview Person Detection
Uniview's smart intrusion prevention and person detection features are designed to provide highly accurate security alerts, filtering out irrelevant motion from things like animals or weather. However, when this technology gets it wrong, it can lead to a flood of false alarms or, worse, missed events.
If you're finding that your Uniview person detection is inaccurate, this troubleshooting guide will walk you through the essential adjustments to improve its performance and restore your peace of mind.
## 1. Calibrate Your Smart Intrusion Prevention (SIP) Settings
The first place to look is within the camera's VCA (Video Content Analysis) settings. This is where you control how the AI analyses the scene.
### Define a Precise Detection Area
Avoid setting the entire screen as the detection zone. The more focused the area, the better the AI can perform.
- Draw a specific box or polygon around the area of interest, such as a doorway, a garden path, or a driveway.
- Exclude busy backgrounds. Do not include public footpaths, busy roads, or neighbours' gardens in your detection zone.
- Avoid foliage. Exclude trees, bushes, and large plants that sway in the wind, as their movement can sometimes be misinterpreted.
### Adjust Sensitivity and Object Size
- Sensitivity: This setting determines how much change is needed to trigger an event. The default is often around 50. If you are getting false alarms from shadows or rain, try lowering the sensitivity to 40 or 30. If you are missing real events, you may need to increase it slightly.
- Object Size: Many Uniview cameras allow you to set a minimum and maximum object size for detection. This is extremely powerful. Draw a box that is roughly the size of a person at the distance you expect to detect them. This tells the system to ignore objects that are much smaller (like cats or birds) or much larger (like a car pulling into the driveway).
## 2. Optimise Camera Placement and Field of View
The physical installation of the camera plays a massive role in the accuracy of any AI-based detection.
### Angle is Everything
- Avoid eye-level views: Do not mount the camera looking straight out at the horizon. This perspective makes it difficult for the AI to distinguish human shapes from other objects.
- Mount high, aim low: The ideal placement is 2.5 to 3 metres (8-10 feet) high, angled downwards. This high-angle view provides a clear silhouette of a person's head and shoulders, which is what the AI is trained to recognise.
### Ensure Proper Lighting
- Avoid strong backlighting: Do not point the camera directly at a bright light source, such as a security light or the rising/setting sun. This can create silhouettes and reduce the detail needed for accurate analysis.
- Sufficient Night Vision: For night-time detection, ensure the camera's built-in IR illuminators are powerful enough to light up your entire detection zone. If the area is too dark, the AI will struggle. Consider adding an external IR illuminator for large areas.
## 3. Perform Essential System Maintenance
Finally, ensuring your system's software is up to date is crucial for a reliable performance.
### Update Your Firmware
Uniview constantly refines its detection algorithms. A firmware update can provide a significant boost in accuracy.
- Check the firmware version for both your camera and your NVR.
- Visit the official Uniview website to download the latest firmware for your specific models.
- Follow the instructions carefully to perform the update. This is one of the most effective ways to solve persistent false alert issues.
### Clean the Lens
A smudged or dirty camera lens can distort the image and confuse the AI. Cobwebs, dust, and raindrops can all trigger false person detection alerts. Regularly clean the lens with a soft, microfibre cloth to ensure the camera has a crystal-clear view of the scene.
By systematically working through these configuration, placement, and maintenance steps, you can dramatically reduce false alerts and increase the reliability of your Uniview person detection system.