8 months ago

ACC 6 Enterprise Datasheet

• Outdoor High

• 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

It is highly recommended that the self-learning feature be enabled for all video analytics devices, except in the following circumstances: • If you do not expect any humans or vehicles in the device's field of view. • If humans and vehicles in the field of view move at multiple height levels, such as people on a staircase. If the scene changes significantly, you may want to reset the self-learning settings. When self-learning is reset, all previous self-learning data is deleted and the device learns anew. You may want to use this feature if a building in the scene is demolished then rebuilt. Enabling Self-Learning The Video Analytics Configuration dialog box allows you to enable or disable self-learning in video analytics devices. 1. In the device Setup tab, click . The Video Analytics Configuration dialog box opens. 2. To enable self-learning, check the Enable Self-Learning box. 3. To disable self-learning, clear the Enable Self-Learning check box. NOTE: Disabling self-learning may result in more classified objects being falsely detected. Once disabled, the camera stops self-learning and no longer utilizes any learned information. 4. To reset self-learning, click Self-Learning Reset. • In the confirmation dialog box that appears, click Yes. NOTE: When self-learning is reset, all previous self-learning data for the device is deleted. 5. Click OK to save your changes. Teach By Example You can improve the accuracy of classified object detection by using the Teach By Example feature. You can assign true or false Teach Markers to detected objects to help train the video analytics device. Teach Markers can be assigned then applied to devices by different users. Users who assign markers to detected objects are typically users who monitor video on a regular basis. It is recommended that 30 true and 30 false markers be assigned before they are applied to a device. Users who apply the markers to the device may be an administrator who is less involved with day to day video monitoring. NOTE: The Teach Markers are local to a single server and are created for individual cameras. They are not shared between servers or cameras. NOTE: Some features are not displayed if the server does not have the required license, or if you do not have the required user permissions. Enabling Self-Learning 69

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