Calibrating Your Axis Camera for Accurate Person Detection
Axis cameras, equipped with powerful analytics like AXIS Object Analytics, are engineered to provide intelligent and reliable security monitoring. However, the accuracy of advanced features like person detection is highly dependent on proper setup and calibration. If you're experiencing a high rate of false alarms or missed detections, this guide will help you troubleshoot and fine-tune your system for professional-grade performance.
### Step 1: Foundational Camera Calibration
Before you even touch the detection zone settings, you must ensure the camera's perspective is correctly configured. The analytics engine needs to understand the geometry of the scene to accurately judge the size and shape of objects.
- Access the Analytics Settings: Log in to your camera's web interface and navigate to the settings for your analytics application (e.g., AXIS Object Analytics).
- Run the Calibration Process: Find the calibration tool within the application.
- Enter Mounting Height: Accurately measure and input the camera's physical mounting height from the ground. This is a critical parameter.
- Set the Horizon and Perspective: The tool will likely display an overlay on the video feed. Adjust the lines to match the horizon and the perspective of parallel lines in the scene (like the edges of a road or building). This teaches the algorithm about distance and scale. An object should appear smaller as it moves further away, and calibration ensures the system understands this.
Step 2: Strategic Configuration of Detection Zones
Detection zones tell the analytics engine where to look. Thoughtful configuration is key to eliminating false positives.
### Use Include and Exclude Zones
- Include Zones: Draw these zones to cover the specific areas where you need to detect people. This could be a path leading to a door, a car park, or a restricted area. Be as precise as possible.
- Exclude Zones: This is your most powerful tool for reducing false alarms. Draw exclude zones over any areas that cause unwanted triggers. Common examples include:
- Public pavements or roads visible in the background.
- Bushes, trees, or flags that move in the wind.
- Areas with reflective surfaces that can cause light changes, like puddles.
Step 3: Fine-Tuning Object and Filter Settings
Once your zones are set, you can refine the detection parameters.
### Adjust Object Size Filters
- Minimum and Maximum Size: Many Axis applications allow you to set filters for object size. Configure the "small object" filter to ignore animals like cats or foxes. Set the "large object" filter to ignore irrelevant large items like passing lorries, if necessary. You may need to observe the scene for a while to determine the appropriate pixel size for people at various distances.
- Dwell Time: Set a minimum "time in scene" or "dwell time" to prevent momentary triggers from things like fast-moving car headlights or shadows. A setting of 1-2 seconds can ensure the object is persistently in the zone before an alarm is raised.
### Optimise for Environmental Conditions
- Lighting: Ensure the monitored area is well-lit, especially during night-time operation. Poor illumination or high-contrast shadows make detection difficult. Use sufficient infrared (IR) or white light illuminators.
- Keep the View Clear: Regularly clean the camera's dome or window. A smudged or dirty lens can severely degrade the performance of any video analytic.
By systematically calibrating the camera's perspective, carefully defining detection zones, and tuning the object filters, you can transform your Axis camera from a simple motion detector into a highly accurate and reliable security tool.