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Tip

Video stream and scene requirements for the Object Presence DetectionNeural classifier

Hardware requirements for neural analytics operation

To configure the Object Presence Detection Neural classifier, do the following:

  1. Go to the Detection Tools Detectors tab.
  2. Below the required camera, click Create… → Category: Production Safety → Object Presence DetectionNeural classifier.

By default, the detection tool detector is enabled and set to detect objects in the frame.

If necessary, you can change the detection tool detector parameters. The list of parameters is given in the table:

ParameterValueDescription
Object features
Record mask to archiveYesBy default, the sensitivity scale of the detection tool detector is recorded to the archive (see Displaying information from a detection tool detector (mask)). To disable the parameter, select the No value
No
Video streamMain streamIf the camera supports multistreaming, select the stream for which detection is needed. Selecting a low quality video stream reduces the load on the Server
Other
EnableYesThe detection tool detector is enabled by default. To disable the detection tooldetector, select the the No value
No
NameObject Presence DetectionNeural classifierEnter the detection tool detector name or leave the default name
Decoder modeAutoSelect 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 used for decoding
CPU
GPU
HuaweiNPU
Number of frames processed per second0.1Specify the number of frames that the detection tool detector will process per second. The value must be in the range range [0.016; 100]
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.

    1. If you leave the field blank, the tracks of all available classes from the neural network will be displayed (Detection neural network, Neural network file).
    2. If you specify a class/classes from the neural network, the tracks of the specified class/classes will be displayed (Detection neural network, Neural network file).
    3. 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 displayed (Detection neural network, Neural network file).

    4. If you specify a class/classes missing from the neural network, the tracks won’t be displayed (Detection neural network, Neural network file)

TypeObject Presence DetectionNeural classifierName of the detection tool detector type (non-editable field)
Advanced settings
Neural network file

Select a Specify the pathto the neural network file. The standard neural networks for different processor types are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directory. You don't need to select the standard neural networks in this field, the system will automatically select the required one. If you use a custom neural network, enter a path to the file.

Note
titleAttention!
  • 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).
  • To ensure the correct operation of the neural network on Linux OS, the corresponding file must be located
Info
titleNote
For the correct neural network operation on Linux OS, place the corresponding file
  • in the /opt/AxxonSoft/DetectorPack/NeuroSDK directory. 
Number of measurements in a row to trigger detection5

Specify the minimum number of frames on which the detection tool detector must detect an object to generate an event. The value must be in the range [5; 20]

Scanning modeYes

The parameter is disabled by default. To detect objects without changing the frame size, select the Yes value. To work in the scanning mode, the neural network must support the scanning mode

No
Basic settings
ModeCPU

Select a processor for the neural network operation (see Hardware requirements for neural analytics operation, Selecting Nvidia GPU when configuring detectors).

Note
titleAttention!
Nvidia GPU 0
Nvidia GPU 1
Nvidia GPU 2
Nvidia GPU 3
Intel GPU
Huawei NPU
Sensitivity 33

Specify the sensitivity of the detection tool detector empirically. The value must be in the range [1; 99].  The The preview window displays the sensitivity scale of the detection tool detector that relates to the sensitivity parameter. If the scale is green, object isn't detected. If the scale is yellow, object is detected, but not enough to generate an event. If the scale is red, object is detected and the detection tool detector will generate an eeventevent, if the scale is red through the sampling period (50 seconds by default).


Example. The sensitivity parameter value of 40 means that the detection tool detector will generate an event when the scale has at least four divisions full over the entire detection period. An event will stop when the scale has less than two divisions full over the detection period. The detection tool detector will generate an event again if the scale has at least four divisions full over the entire detection period

...

  1. Right-click in the preview window.
  2. If you want to specify the detection area by one or more rectangles, select Detection area (rectangle). If you specify a rectangular area, the detection tool detector will analyze only this area. The rest of the frame will be ignored.
  3. If you want to specify the detection area by one or more polygons, select Detection area (polygon). If you specify one or several polygonal areas, the detection tool detector will analyze the entire frame. The part of the frame not included in the specified polygons will be blacked out.

    Note
    titleAttention!

    You must select the detection area (polygon or rectangle) experimentally. For some neural networks the quality of detection will be better with rectangle, for others—with polygon.

...

To save the parameters of the detection tooldetector, click the Apply button. To cancel the changes, click the Cancel  button.

Configuration of the Neural classifier is complete.