How to Fix Incorrect Person Detection on Your Dahua Camera
One of the most powerful features of modern Dahua security cameras is the ability to distinguish people from other moving objects, reducing the flood of irrelevant notifications. However, when this person detection feature gets it wrong, it can be frustrating, sending you false alerts for swaying trees or failing to spot an actual person.
If you're getting alerts for animals and shadows, or no alerts at all, don't worry. This is a common issue that can usually be fixed by adjusting a few key settings. This guide will walk you through the steps to fine-tune your Dahua camera for accurate and reliable person detection.
## Understanding Dahua's AI: SMD and Perimeter Protection
Dahua uses advanced AI, often referred to as Smart Motion Detection (SMD) or as part of their Perimeter Protection features (like Tripwire and Intrusion). Unlike traditional motion detection that reacts to any pixel change, these systems are designed to recognise the specific shapes of humans and vehicles. Getting this right is key to reducing false alarms.
Common reasons for incorrect detection include:
- Sensitivity settings being too high or too low.
- Complex environments with confusing shadows or moving objects.
- Improperly drawn detection rules (tripwires or intrusion zones).
- Outdated camera firmware.
## Step 1: Update Your Camera's Firmware
Before you start changing settings, it's crucial to ensure your camera is running the latest firmware. Dahua frequently releases updates that improve the performance and accuracy of their AI algorithms.
- Log in to your camera's web interface using its IP address.
- Navigate to the System or Maintenance section.
- Check for a Firmware Update or Upgrade option.
- Follow the instructions to download and install the latest version from the official Dahua website. A firmware update can often solve detection problems immediately.
## Step 2: Calibrate Your Detection Settings
This is where you can make the biggest impact on accuracy. You will need to access your camera's settings, typically through the web interface or a connected NVR.
### Adjusting Sensitivity
The sensitivity setting determines how much an object needs to resemble a person before an alert is triggered.
- If you get too many false alerts (e.g., for pets or shadows): Your sensitivity is likely too high. Try reducing it in small increments.
- If the camera misses actual people: Your sensitivity may be too low. Increase it slightly and test again.
### Refining Detection Rules
How you define the detection area is critical.
- Use Tripwire for Clear Boundaries: A tripwire is a virtual line. It's best for clear entry points like doorways or across a driveway. An alert is triggered only when a person crosses this line. This is often more reliable than a large detection box.
- Use Intrusion for Broader Areas: An intrusion zone is a defined box (e.g., drawn around your porch). An alert is triggered when a person enters or remains in this box.
- Avoid "Noisy" Backgrounds: When drawing your rules, try to exclude areas with constant irrelevant movement, such as a public pavement, rustling bushes, or areas with dramatic shadow changes throughout the day.
- Target Filtering: Ensure that within the rule settings, you have selected "Human" as the target object. Unchecking "Vehicle" (if you don't need it) can further refine the focus.
## Step 3: Optimise Camera Placement and Lighting
The physical position of your camera plays a huge role in the AI's ability to work correctly.
- Angle and Height: The ideal position is a slight downward angle, not a top-down "bird's-eye" view. The AI is best at recognising human shapes from a more direct perspective.
- Lighting: Strong backlighting (e.g., pointing directly at the rising or setting sun) can create silhouettes that are difficult for the AI to analyse. Ensure the area you want to monitor is as evenly lit as possible, especially at night. Good infrared (IR) night vision is essential.
- Obstructions: Make sure there are no tree branches, flags, or other objects moving in the foreground that could partially obscure a person or trigger false events.
## Step 4: Test and Iterate
Fine-tuning person detection is a process of trial and error. After making an adjustment, monitor the alerts for a day or two to see how it has affected performance. It may take a few small tweaks to find the perfect balance between responsiveness and accuracy for your specific environment. By systematically working through these steps, you can significantly reduce false alarms and trust that you'll be notified when it truly matters.