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Configuring the
...
detector
To configure configure the Neural counter detector, do the following:
- Go to the
Detection Tools tab- Detectors tab.
Below the required camera, click Create… → Category: Retail → Neural counter.
By default, the detection tool detector is enabled and set to count the number of objects in a specified area using a neural network.
If necessary, you can change the detection tool detector parameters. The list of parameters is given in the table:
| Parameter | Value | Description |
|---|
| Object features |
| Record mask to archive | Yes | By default, the recording of the mask to the archive is disabled. To record the sensitivity scale of the |
detection tool | detector to the archive ( |
see detection tool | detector is enabled. To disable, select |
the | the No value |
| No |
| Name | Neural counter | Enter the |
detection tool | detector name or leave the default name |
| Decoder mode | Auto | Select a processing resource for decoding video streams. When you select a GPU, a stand-alone graphics card takes priority (when decoding with Nvidia NVDEC chips). If there is no appropriate GPU, the decoding will use the Intel Quick Sync Video technology. Otherwise, CPU resources |
will be | are used for decoding |
| CPU |
| GPU |
| HuaweiNPU |
| Number of frames processed per second | 1 | Specify the number of frames for the |
detection tool detector to process per second. The value must be in the range [0.016, 100] | Info |
|---|
| The default values (3 output frames and 1 FPS) mean |
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that that the Neural counter analyzes one frame once per second. If the Neural counter detects the specified number of objects (or more) on 3 frames, an event from the |
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detection tool |
| Type | Neural counter | Name of the |
detection tool | detector type (non-editable field) |
| Advanced settings |
| Detected objects | Yes | By default, detected objects aren't highlighted in the preview window. If you want to highlight detected objects, select the Yes value |
| No |
| Neural network file |
| If you use a custom neural network, select the corresponding file | Note |
|---|
| - To train your neural network, contact AxxonSoft (see Data collection requirements for neural network training).
- A trained neural network for a particular scene allows you to detect only objects of a certain type (for example, a person, a cyclist, a motorcyclist, and so on).
- If you don't specify the neural network file
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is not specified- , the default file is used that is selected automatically depending on the selected value in
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the - the Detection neural network parameter and the selected processor for the neural network operation in the Decoder mode parameter. If you use a custom neural network, enter a path to the file. The selected detection neural network is ignored when you use a custom neural network.
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To ensure the correct operation of the neural network on Linux OS, the corresponding file must be located in the /opt/AxxonSoft/DetectorPack/NeuroSDK directory. If you use standard - If you use a standard neural network (training wasn't performed in operating conditions), we guarantee the overall accuracy of 80-95% and the percentage of false positives of 5-20%. The standard neural networks are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directory.
- You cannot specify the network file in Windows OS. You must place the neural network file locally, that is, on the same server where you install Axxon One.
- For correct neural network operation on Linux OS, place the corresponding file locally in the /opt/AxxonSoft/DetectorPack/NeuroSDK directory or in the network folder with the corresponding access rights.
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| Number of measurements in a row to trigger detection | 3 | Specify the minimum number of frames on which the |
detection tool | detector must detect a violation |
for the detection tool | to generate an event. The value must be in the range [1, 20] |
| Selected object classes |
| If necessary, specify the class of the detected object. If you want to display tracks of several classes, specify them separated by a comma with a space. For example, 1, 10.
The numerical values of classes for the embedded neural networks: 1—Human/Human (top-down view), 10—Vehicle. - If you leave the field blank, the tracks of all available classes from the neural network
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will be - are displayed (Detection neural network, Neural network file).
- If you specify a class/classes from the neural network, the tracks of the specified class/classes
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will be - are displayed (Detection neural network, Neural network file).
- If you specify a class/classes from the neural network and a class/classes missing from the neural network, the tracks of a class/classes from the neural network
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will be - are displayed (Detection neural network, Neural network file).
If you specify a class/classes missing from the neural network, the tracks of all available classes from the neural network
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will be are displayed (Detection neural network, Neural network file)
| Info |
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| Starting with Detector Pack 3.10.2, if you specify a class/classes missing from the neural network, the tracks |
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won’t be aren't displayed (Detection neural network, Neural network file). |
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| Scanning window | Yes | If detection of small objects or objects in areas far away from the camera is ineffective, you can use the scanning mode. The scanning mode doesn’t provide absolute detection accuracy, but it can improve detection performance. To enable the scanning mode, select the Yes value (see Configuring the scanning mode) |
| No |
| Scanning window height | 0 | The height and width of the scanning window are determined according to the actual size of the frame and the required number of windows. For example, the real frame size is 1920×1080 pixels. To divide the frame into four equal windows, set the width of the scanning window to 960 pixels and the height to 540 pixels |
| Scanning window step height | 0 | The scanning step determines the relative offset of the windows. If the step is equal to the height and width of the scanning window respectively, the segments will line up one after another. Reducing the height or width of the scanning step will increase the number of windows due to their overlapping each other with an offset. This will increase the detection accuracy, but will also increase the CPU load. | Note |
|---|
| The height and width of the scanning step must not be greater than the height and width of the scanning window—the |
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detection tool will not detector won't operate with such settings. |
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| Scanning window step width | 0 |
| Scanning window width | 0 | The height and width of the scanning window are determined according to the actual size of the frame and the required number of windows. For example, the real frame size is 1920×1080 pixels. To divide the frame into four equal windows, set the width of the scanning window to 960 pixels and the height to 540 pixels |
| Basic settings |
| Detection threshold | 30 | Specify the Detection threshold |
for If | If the recognition probability falls below the specified value, the data |
will be | is ignored. The higher the value, the higher the accuracy, |
but detection tool | detector may not be considered. The value must be in the range [0.05, 100] |
Mode
| CPU | |
other - another processing resource than the CPU, this device
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will carry the - carries most of the computing load. However, the CPU
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will be detection tool with 3- 3.11, Intel HDDL and Intel NCS aren’t supported.
- Starting with Detector Pack 3.14, Intel Multi-GPU and Intel GPU 0-3 are supported.
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| Nvidia GPU 0 |
| Nvidia GPU 1 |
| Nvidia GPU 2 |
| Nvidia GPU 3 |
| Intel NCS (not supported) |
| Intel HDDL (not supported) |
| Intel GPU |
| Intel Multi-GPU |
| Intel GPU 0 |
| Intel GPU 1 |
| Intel GPU 2 |
| Intel GPU 3 |
| Huawei NPU |
| Number of alarm objects | 5 | Specify the number of objects at which an event occurs. The value must be in the range [0, 100] |
| Detection neural network | Person | Select the detection neural network from the list. Neural networks are named taking into account the objects they detect. The names can include the size of the neural network (Nano, Medium, Large), which indicates the amount of consumed resources. The larger the neural network, the higher the accuracy of object recognition |
| Person (top-down view) |
| Person (top-down view Nano) |
| Person (top-down view Medium) |
| Person (top-down view Large) |
| Vehicle |
| Person and vehicle (Nano) |
| Person and vehicle (Medium) |
| Person and vehicle (Large) |
| Trigger to | Greater than or equal to threshold value | Select when you want to generate an event. The Neural counter will generate events from the threshold value set in the Number of alarm objects field |
| Less than or equal to threshold value |
| Change in readings |
By default, the entire frame is the detection area. In the preview window, you can specify the detection areas using the anchor points 
Image Modified (see Configuring a detection area).
Image Modified
| Info |
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- For convenience of configuration, you can "freeze" the frame. Click the
 Image Modified button. To cancel the action, click this button again. - The detection area is displayed by default. To hide it, click the
 Image Modified button. To cancel the action, click this button again.
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To save the parameters of the detection tooldetector, click the Apply 
Image Modified button. To cancel the changes, click the Cancel 
Image Modified button.
Configuring the Neural counter detector is complete. It is possible to display the sensor and the number of objects in the monitored area in the Surveillance window on the layout (see see Displaying the number of detected objects).
Example of configuring the Neural counter detector for solving typical tasks
By default, the Neural counter is set to detect objects with a speed less than 0.3 m/s:
| Parameter | Value |
|---|
| Other |
| Number of frames processed per second | 1 |
Advanced settings
|
| Number of measurements in a row to trigger detection | 3 |
| Neural network file | Path to the *.ann neural network file. You can also select the value in the Detection neural network parameter. In this case, this field must be left blank |
| Basic settings |
| Detection threshold | 30 |
To solve tasks in which object speed differs from 0.3 m/s, you must increase Number of frames processed per second or/and decrease Number of measurements in a row to trigger detection. You must select the values empirically depending on the task conditions.
