Is Your Avigilon System Seeing Things That Aren't There?
Avigilon's powerful video analytics are designed to be a force multiplier for your security team, intelligently distinguishing between routine motion and significant events, like a person entering a restricted area. However, when this sophisticated system starts generating false alerts—classifying a stray animal as a person or vice-versa—it can lead to alarm fatigue and a loss of confidence in the system.
If you're struggling with inaccurate person detection, this guide is for you. We'll explore why these inaccuracies happen and how you can fine-tune your Avigilon Control Center (ACC) setup for the precise, reliable results you expect.
Understanding Avigilon's Pattern-Based Analytics
Unlike simple motion detection, Avigilon's system uses advanced, self-learning algorithms to analyse object characteristics and movement patterns. It learns what constitutes normal background activity in a scene and then flags deviations from that norm. For classification, it's been taught to recognise the specific attributes of humans and vehicles. However, the accuracy of this process is highly dependent on a clean setup.
Key factors that influence accuracy include:
- Camera Placement: The camera's height, angle, and field of view are critical.
- Environmental Conditions: Poor lighting, shadows, rain, and snow can all impact performance.
- Scene Complexity: A "busy" scene with lots of irrelevant movement is harder to analyse.
- System Configuration: The settings within ACC must be optimised for the specific scene.
Steps to Improve Person Detection Accuracy
Optimising your analytics is a process of refinement. Start with these fundamental steps.
1. Evaluate Camera Installation and Field of View
You can't fix a bad view with software.
- Optimal Angle: Ensure the camera is not positioned at too steep an angle. A camera looking straight down from a high ceiling will struggle to see the difference between a person and a large box.
- Sufficient Pixels on Target: For reliable classification, an object (the person) needs to be large enough in the frame. If you're trying to detect people at a great distance, you may need a camera with a higher resolution or a longer lens.
- Stable Image: The camera must be mounted securely. Vibration from wind or machinery can severely degrade analytics performance.
2. Fine-Tune Your Analytic Event Rules in ACC
This is where you tell the system what you care about.
- Define Regions of Interest (ROI): Don't analyse the entire scene if you don't have to. Draw a specific region where you expect to detect people, such as a doorway or a walkway, and exclude areas like public roads or rustling trees.
- Set Object Size Parameters: In the rule configuration, you can often specify the minimum and maximum size of an object to be classified as a person. This is a powerful tool to exclude things like small animals or large vehicles.
- Use Beam-Crossing and Other Rules: Instead of just "person in area," use more specific rules like "person crosses this line" for more targeted and accurate alerting.
3. Calibrate the Camera Scene
Forcing the system to re-learn the environment can resolve many issues. In the camera's analytics settings within ACC, you will find an option to "Calibrate" or "Reset Analytics." This prompts the camera to re-evaluate the scene, learn the background, and can often clear up persistent classification errors.
4. Keep Your ACC Software Updated
Avigilon is constantly refining its analytics algorithms. Ensuring your ACC server and cameras are running the latest supported firmware and software versions is crucial to benefit from these improvements.
By methodically reviewing your camera placement and carefully configuring the rules within ACC, you can dramatically reduce false positives and transform your Avigilon system into the intelligent and reliable security partner it's meant to be.