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Configuration of the Neurocounter module includes: configuring the detector and selecting the area of interest. You can configure the Neurocounter module on The Neurocounter module can be configured on the settings panel of the Neurocounter object created on the basis of the Camera object on the Hardware tab of the System settings dialog window.

The Neurocounter module is configured as follows:

Configuring the detector

1. Go to the Neurocounter object settings panel.
   Image Removedthe settings panel of the Neurocounter object.
   Image Added
2. Set the Show objects on image checkbox to frame the detected object on the video image in the debug window (see Start the debug window).
3. From the Camera position drop-down list, select:
   1. Wall—objects are detected only if their lower part gets into the area of interest specified in the detector settings.
   2. Ceiling—objects are detected even if their lower part doesn't get into the area of interest specified in the detector settings.
4. In the Number of frames for analysis and output
 field (1)
5. field, specify the number of frames
to
6. that must be processed to determine the number of objects on them.
7. In the Frames processed per second [0
,
8. .016, 100] field
(2)
9. ,
set
10. specify the number of frames
per second that will be processed by the detection tool
11. processed per second by the neural network in the range from 0.016 to 100. For all other frames interpolation is performed—finding intermediate values by the available discrete set of its known values. The greater the value of the parameter, the more accurate the detector operation, but the higher the load on the processor.
12. From the Send event drop-down list
(3)
13. , select the condition by which an event with the number of detected objects
will be
14. is generated:
    • If threshold exceeded
-
15. • is
triggered
16. • generated if the number of detected objects in the image is greater than
or equal to
17. • the value specified in the Alarm objects count field
.
18. • ;
    • If threshold not reached
-
19. • is
triggered
20. • generated if the number of detected objects in the image is less than
or equal to
21. • the value specified in
the 
22. • the Alarm objects count field
.
23. • ;
    • On count change
-
24. • is
triggered
25. • generated every time the number of detected objects changes
.
26. • ;
    • By period
-
27. • is
triggered
28. • generated by
a
29. • the time period:
      1. In the Event periodicity field, specify the time after which the event with the number of detected objects is generated. The range of values: from 1 to 100—for seconds, minutes, hours; from 1 to 20—for days.
      2. From the
Event periodicity 
30. • 1. Time interval drop-down list
(4)
31. • 1. , select the time unit of the counter period: seconds, minutes, hours, days.
32. In the Event periodicity field (4), set the time after which the event with the number of detected objects will be generated.
In the 
33. • 1. 
         

         Info
         title Note
         If the entered value exceeds the allowable range, then after you click the Apply button, the maximum value is set automatically.

34. In the Alarm objects count field
(5)
35. ,
set
36. specify the threshold number of detected objects in the area of interest. It is used in the If threshold exceeded and If threshold not reached conditions. The default value is 5.

37. In the Recognition threshold [0, 100]

 field (6)

38. field, enter the

neural counter sensitivity –

39. neurocounter sensitivity—an integer value in the range from 0 to 100. The default value is 30.

    Info
    title Note

The neural counter

40. The neurocounter sensitivity is determined experimentally. The lower the sensitivity, the higher the

more false triggerings there might be

41. probability of false alarms. The higher the sensitivity, the

less false triggerings there might be

42. lower the probability of false alarms, however, some useful tracks

might

43. can be skipped. See Example of configuring neurocounter for solving typical tasks.

44. Set the Scanning mode checkbox to detect small objects. If you enable this mode, the load on the system increases. So in step 5 we recommend specifying a small number of frames processed per second. By default, the checkbox is cleared. For more information on the scanning mode, see Configuring the Scanning mode.
45. By default, the standard (default) neural network is initialized according to the object type selected in step 14 and the device type in step 13. The standard neural networks for different processor types are selected automatically; you must not do it manually. If you use a custom neural network, then click the Image Added button to the right of the .Click the Image Removed button (7), Tracking model field, and in the standard Windows box that opens, select the neural network file with the neural counter model.Explorer window that opens, specify its file.
    

    Note
    title Attention!
    To train a neural network, contact the AxxonSoft technical support (see Data collection requirements for neural network training). A neural network trained for a specific scene allows detecting objects of a certain type only (for example, a person, cyclist, motorcyclist, and so on).

46. Set the Model quantization checkbox to enable themodel quantization. By default, the checkbox is cleared. This parameter allows reducing the consumption of the GPU's computational power.
    

    Info
    title Note
    AxxonSoft conducted a study in which a neural network model was trained to identify the characteristics of the detected object. The following results of the study were obtained: model quantization can lead to both an increase in the percentage of recognition and a decrease. This is due to the generalization of the mathematical model. The difference in detection percentage ranges within ±1.5%, and the difference in object identification ranges within ±2%. Model quantization is only applicable for NVIDIA GPUs. The first launch of the detector with the activated quantization feature can take longer than a standard launch. If you use the GPU caching, next time the detector with quantization runs without delays.

47. If necessary, specify the class of the detected object in the Target classes field. If you want to count and 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 view), 10—Vehicle.
    

    Info
    title Note
    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 are counted and displayed (Object type, Neural network file). If you specify a class/classes missing from the neural network, tracks aren't counted and displayed.

48. From the Device drop-down listIn the Device drop-down list (8), select the device on which the neural network will operate.the neural network operates: CPU, one of NVIDIA GPUs, or one of Intel GPUs. Auto (default value)—the device is selected automatically: NVIDIA GPU gets the highest priority, followed by Intel GPU, then CPU.
    

    Note
    title Attention!
    We recommend using the GPU. It can take several minutes to launch the algorithm on the NVIDIA GPU after you apply the settings. You can use caching to speed up future launches (see Optimizing the operation of neural analytics on GPU).

49. From the Object type drop-down list, select the object type:
    • Human—the camera is directed at a person at an angle of 100-160°;
    • Human (top-down view)—the camera is directed at a person from above at a slight angle;
    • Vehicle—the camera is directed at a vehicle at an angle of 100-160°;
    • Person and vehicle (Nano)—person and vehicle recognition, small neural network size;
    • Person and vehicle (Medium)—person and vehicle recognition, medium neural network size;
    • Person and vehicle (Large)—person and vehicle recognition, large neural network size.
      

      Info
      title Note
      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.

Selecting the area of interest

1. Click the Settings button. As a result, the detection settings window opens.
   Image Added
2. Click the Stop video button (1) in the Detection settings window to pause the playback and capture the frame of the video image.
3. Specify the detection surveillance area on the video image:
4. Click the Settings button (11). The Detection settings window will open.
   Image Removed
5. Click the Stop video button to capture the video image (1).
Click the Click the Area of interest button (2) to specify the area of interest. The button is highlighted in blue.Specify area on which fire/smoke recognition will be detected (3)
Image Added

6. On the captured frame, sequentially set the anchor points of the area in which the objects are detected by using the mouse (3). The rest of the frame is faded. If you don't specify the area of interest, the entire frame is analyzed.
   

   Info
   title Note
   You can add only one area of interest. If you try to add a second area, the first one is deleted. To delete an area, click the Image Added button to the right of the Area of interest button.

7. Click the OK
 button
8. button (4) to save the detector settings and return to the settings panel of the Neurocounter object.
9. Click the
Apply button (12)
10. Apply button to save the changes.

Configuring the Neurocounter module is complete.