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Configuration of the Neurocounter module includes: configuring the detection tool, detector and selecting the area of interest. You can configure the Neurocounter module 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.

Image Modified

Configuring the

...

detector

1. Go to the settings panel of the Neurocounter object.
   
2. Set the Show objects on image checkbox to checkbox to frame the detected objects 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
detection tool
4. 1. detector settings.
   2. Ceiling—objects are detected even if their lower part doesn't get into the area of interest specified in the
detection tool
5. 1. detector settings.
6. In the Number of frames for analysis and output field, specify the number of frames
to
7. that must be processed to determine the number of objects on them.
8. In the Frames processed per second [0.016, 100] field, specify the number of frames processed per second by the neural network in the range from 0.016 to 100. For all other frames interpolation
will be
9. 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
detection tool
10. detector operation, but the higher the load on the processor.
11. From the Send event drop-down list, select the condition by which an event with the number of detected objects
will be
12. is generated:
    • If threshold exceeded is
triggered
13. • generated if the number of detected objects in the image is greater than the value specified in the Alarm objects count field
.
14. • ;
    • If threshold not reached is
triggered
15. • generated if the number of detected objects in the image is less than the value specified in the Alarm objects count field
.
16. • ;
    • On count change is
triggered
17. • generated every time the number of detected objects changes
.
18. • ;
    • By period is
triggered
19. • generated by
a
20. • the time period:
      1. In the Event periodicity field, specify the time after which the event with the number of detected objects
will be generated
21. • 1. is generated. The range of values: from 1 to 100—for seconds, minutes, hours; from 1 to 20—for days.
      2. From the Time interval drop-down list, select the time unit of the counter period: seconds, minutes, hours, days.
         
In the 

22. • 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.

23. In the Alarm objects count field, 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.

24. In the Recognition threshold [0, 100] field, enter the neurocounter

sensitivity—integer

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

    Info
    title Note
    The neurocounter sensitivity is determined experimentally. The lower the sensitivity, the higher the probability of false alarms. The higher the sensitivity, the lower the probability of false alarms, however, some useful tracks can be skipped

(see 

26. . See Example of configuring

Neurocounter

27. neurocounter for solving typical

task)

28. tasks.

29. 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 in the Frames processed per second [0.016, 100] field. By default, the checkbox is clearcleared. For more information on the scanning mode, see Configuring the Scanning mode.
30. By default, the standard (default) neural network is initialized according to the object type selected in the Object type drop-down list step 14 and the device selected in the Device drop-down listtype 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 button to the right of the Tracking model field, and in the standard Windows Explorer window that opens, specify the path to the 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 you to detect detecting objects of a certain type only (for example, a person, cyclist, motorcyclist, and so on).

31. Set the Model quantization checkbox to enable themodel quantization. By default, the checkbox is clearcleared. This parameter allows you to reduce reducing the consumption of the GPU processing '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 a detection tool with quantization enabled may the detector with the activated quantization feature can take longer than a standard launch. If you use the GPU caching is used, next time a detection tool the detector with quantization will run runs without delaydelays.

32. 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 will be are counted and displayed (Object type, Neural network file). If you specify a class/classes missing from the neural network, tracks wonaren't be counted and displayed.

33. From the Device drop-down list, select the device on which the neural the neural network will operateoperates: 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 may 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).

34. From the Object type drop-down list, select the object type:
    • Human—the camera is directed at a person at
the
35. • an angle of 100-160°
.
36. • ;
    • Human (top-down view)—the camera is directed at a person from above at a slight angle
.
37. • ;
    • Vehicle—the camera is directed at a vehicle at
the
38. • 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. The Detection As a result, the detection settings window opens.
   
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. Click the Area of interest button (2) to specify the area of interest. The button will be is highlighted in blue.
   
   
4. On the captured frame, sequentially set the anchor points of the area in which the objects
will be detected
5. are detected by using the mouse (3). The rest of the frame
will be
6. 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

will be

7. is deleted. To delete an area, click the Image Modified button to the right of the Area of interest button.

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

Configuring the Neurocounter module is complete.