ACC 6 Enterprise Datasheet

• Outdoor High Sensitivity — This setting allows the analytics to run with higher sensitivity for
detecting humans and can be used in more difficult scenes. More frequent false positives may
occur. This setting detects humans and vehicles.
NOTE: If you change the Location: setting after it has been set, the system will delete any data the device
may have learned.
4. In the Camera Type: drop down list, select the type of camera that has been connected to this camera
channel.
This helps the video analytics appliance determine what type of image it should expect from the camera.
• Day and Night — select this option if the camera can stream video in color or black and white. This
type of camera typically displays color video during the day and black and white video at night to
capture as much detail as it can of the scene.
• Color — select this option if the camera can only stream video in color.
• Black and White — select this option if the camera can only stream video in black and white.
• FLIR — select this option if the camera can stream forward looking infrared (FLIR) video.
5. Check the Enable Noise Filter box if the camera is too sensitive and falsely detects motion as classified
objects. Disable this option if the camera is not sensitive enough.
6. Click Apply to save your settings.
7. If you are prompted, allow the device to reboot.
Next, you can choose to enable self-learning and configure analytics events. For more information, see Self-
Learning below or Video Analytics Events on page 72.
Self-Learning
When self-learning is enabled, the video analytics device will perform initial self-calibration for the scene in its
field of view. This can significantly improve the accuracy of human and vehicle classification.
NOTE: Enabling and disabling self-learning does not affect the Teach By Example feature.
What is Self-Learning?
Self-learning is the video analytics device's ability to perform an initial self-calibration of the scene. Once
enabled, self-learning can significantly improve object classification accuracy.
Self-learning is configured from the Video Analytics Configuration dialog box. The Self-Learning Progress: status
in the dialog box tells you the following information:
• 0% - 33% — The device is in the initial learning stage where it begins to gather information on the scene.
• 34% - 66% — The device is calibrating itself using the data it has gathered on the average person and
average vehicle in the scene.
• 67% - 100% — The device has established a high level of classified object detection accuracy.
Tip: To help increase the device's detection accuracy, use the Teach By Example feature. For more information,
see Teach By Example on the next page.
NOTE: If the device continues to display low object detection accuracy after completing self-learning, an error
may have occurred during installation. Contact Avigilon Technical Support for help.
Self-Learning 68