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Starting with Detector Pack 3.14, the parameter is disabled by default. If you need to hide static objects, select the Yes value.

This parameter lowers the number of when detecting moving objects. An object is considered static if it hasn't moved more than 10% of its width or height during the whole time of its track existence
ParameterValueDescription
Object features
Record objects trackingYes

By default, metadata are recorded into the database. To disable metadata recording, select the No value

Note
titleAttention!

To obtain metadata, video is decompressed and analyzed, which places a heavy load on the server and limits the number of cameras used on it.

No
Video streamMain streamIf the camera supports multistreaming, select the stream for which detection is needed
Other
EnableYesBy default, the detector is enabled. To disable, select the No value
No
NameNeural trackerEnter the 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 are used for decoding


CPU
GPU
HuaweiNPU
Number of frames processed per second6

Specify the number of frames for the neural network to process per second. The higher the value, the more accurate the tracking, but the load on the CPU is also higher. The value must be in the range [0.016, 100]

Note
titleAttention!

We recommend the value of at least 6 FPS. For fast-moving objects (running individuals, vehicles), you must set the frame rate at 12 FPS or above.

TypeNeural trackerName of the detector type (non-editable field)
Advanced settings
Camera position
 		Wall To sort out false events from the detector when using a fisheye camera, select the correct device location. For other devices, this parameter is irrelevant
Ceiling
Color detection (starting with Detector Pack 3.14)Hide moving objects
 		Yes

By default, the

color detection

parameter is

enabled. This parameter collects data about the object's color. This data is necessary for further search in the archive by color.
If you set the No value, color detection becomes unavailable when searching in the archive. Disabling the color detection reduces the load on the CPU, including when the detector runs on GPU
No

disabled. If you don't need to detect moving objects, select the Yes value. An object is considered static if it doesn't change its position more than 10% of its width or height during its track lifetime

Note
titleAttention!

If a static object starts moving, the detector creates a track, and the object is no longer considered static.

No
Hide static Hide moving objects
 		Yes

By defaultStarting with Detector Pack 3.14, the parameter is disabled by default. If you don't need to detect moving hide static objects, select the Yes valuethe Yes value.

This parameter lowers the number of false events from the detector when detecting moving objects. An object is considered static if it doesnhasn't change its position moved more than 10% of its width or height during the whole time of its track lifetimeexistence

Note
titleAttention!
  • If a static object starts moving, the detector creates a track, and the object is no longer considered static.
No
  • Disabling the parameter reduces the load on the CPU.
No
Minimum number of detection triggers6Specify the Minimum number of detection triggers for the Neural tracker to display the object's track. The higher the value, the longer the time interval between the detection of an object and the display of its track on the screen. Low values of this parameter can lead to Hide static objects
 		Yes false events from the detector
Note
titleAttention!
  • If a static object starts moving, the detector creates a track, and the object is no longer considered static.
  • Disabling the parameter reduces the load on the CPU.
NoMinimum number of detection triggers6Specify the Minimum number of detection triggers for the Neural tracker to display the object's track. The higher the value, the longer the time interval between the detection of an object and the display of its track on the screen. Low values of this parameter can lead to false events from the detector. The value must be in the range [2, 100]Model quantization
Yes

By default, the parameter is disabled. The parameter is applicable only to standard neural networks for Nvidia GPUs. It allows you to reduce the consumption of computation power. The neural network is selected automatically, depending on the value selected in the Detection neural network parameter. To quantize the model, select the Yes value

Note
titleAttention!

AxxonSoft conducted a study in which a neural network model was trained to identify the characteristics of the detected object with quantization. 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 ranges within ±1.5%, and the difference in object identification ranges within ±2%.

  • The first launch of a detector with the Model quantization parameter enabled can take longer than a standard launch.
  • If GPU caching is used, next time the detector with quantization runs without delay.
. The value must be in the range [2, 100]Model quantization

Yes

By default, the parameter is disabled. The parameter is applicable only to standard neural networks for Nvidia GPUs. It allows you to reduce the consumption of computation power. The neural network is selected automatically, depending on the value selected in the Detection neural network parameter. To quantize the model, select the Yes value

Note
titleAttention!

AxxonSoft conducted a study in which a neural network model was trained to identify the characteristics of the detected object with quantization. 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 ranges within ±1.5%, and the difference in object identification ranges within ±2%.

  • The first launch of a detector with the Model quantization parameter enabled can take longer than a standard launch.
  • If GPU caching is used, next time the detector with quantization runs without delay.


NoNeural network file 

If you use a custom neural network, select the corresponding 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).
  • If you don't specify the neural network file, the default file is used that is selected automatically, depending on the selected value in 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.
  • 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.
Scanning mode

YesBy default, the parameter is disabled. To enable the scanning mode, select the Yes value (see Configuring the scanning mode)
NoScanning window height0The 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 pixelsScanning window step height0

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 are lined up one after another. Reducing the height or width of the scanning step increases the number of windows due to their overlapping each other with an offset. This increases the detection accuracy but can also increase the load on the CPU

Note
titleAttention!

The height and width of the scanning step mustn't be greater than the height and width of the scanning window, since the detector doesn't operate with such settings.

Scanning window width0The 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 pixelsScanning window step width0

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 are lined up one after another. Reducing the height or width of the scanning step increases the number of windows due to their overlapping each other with an offset. This increases the detection accuracy but can also increase the load on the CPU

Note
titleAttention!

The height and width of the scanning step mustn't be greater than

NoNeural network file 

If you use a custom neural network, select the corresponding 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).
  • If you don't specify the neural network file, the default file is used that is selected automatically, depending on the selected value in 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.
  • 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.
Scanning mode
YesBy default, the parameter is disabled. To enable the scanning mode, select the Yes value (see Configuring the scanning mode)
NoScanning window height0The 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 pixelsScanning window step height0

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 are lined up one after another. Reducing the height or width of the scanning step increases the number of windows due to their overlapping each other with an offset. This increases the detection accuracy but can also increase the load on the CPU

Note
titleAttention!

The height and width of the scanning step mustn't be greater than the height and width of the scanning window, since the detector doesn't operate with such settings.

Scanning window width0The 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 pixelsScanning window step width0
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 are lined up one after another. Reducing the height or width of the scanning step increases the number of windows due to their overlapping each other with an offset. This increases the detection accuracy but can also increase the load on the CPU
Note
titleAttention!

The height and width of the scanning step mustn't be greater than the height and width of the scanning window, since the detector doesn't operate with such settings.

since the detector doesn't operate with such settings.

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 are displayed (Detection neural network, Neural network file)
    2. If you specify a class/classes from the neural network, the tracks of the
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 are 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 are 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 are displayed (Detection neural network, Neural network file)

    4. If you specify a class/classes missing from the neural network, the tracks of all available classes from the neural network are displayed (Detection neural network, Neural network file)

      Info
      titleNote

      Starting with Detector Pack 3.10.2, if you specify a class/classes missing from the neural network, the tracks aren't displayed (Detection neural network, Neural network file).

Sensitivity of excluding static objects (starting with Detector Pack 3.14) 25Specify the level of sensitivity of excluding static objects. The higher the value, the less sensitive to motion the algorithm becomes. The value must be in the range [0, 100]Similitude search

Yes

By default, the parameter is disabled. To enable the search for similar persons, select the Yes value. If you enable the parameter, it increases the load on the CPU

Note
titleAttention!

The Similitude search works only on tracks of people.

NoTime of processing similitude track (sec)0Specify the time in seconds for the algorithm to process the track to search for similar persons. The value must be in the range [0, 3600]Time period of excluding static objects0Specify the time in seconds after which the track of the static object is hidden. If the value of the parameter is 0, the track of the static object isn't hidden. The value must be in the range [0, 86 400]Track lifespan (starting with Detector Pack 3.14)

YesBy default, the parameter is disabled. If you want to display the track lifespan for an object in seconds, select the Yes value

NoTrack retention time0.7Specify the time in seconds after which the object track is considered lost. This helps if objects in the scene temporarily overlap each other. For example, when a larger vehicle completely blocks the smaller one from view. The value must be in the range [0.3, 1000]Basic settings
Detection threshold30Specify the Detection threshold for objects in percent. If the recognition probability falls below the specified value, the data will be ignored. The higher the value, the higher the detection quality, but some events from the detector may not be considered. The value must be in the range [0.05, 100]Neural tracker mode













CPU

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

Note
titleAttention!
  • We recommend using the GPU. It can take several minutes to launch the algorithm on an 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 in Windows OS).
  • If the neural tracker is running on the GPU, object tracks can lag behind the objects in the Surveillance window. If this happens, set the camera buffer size to 1000 milliseconds (see Camera).
  • Starting with Detector Pack 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.













Nvidia GPU 0Nvidia GPU 1Nvidia GPU 2Nvidia GPU 3Intel NCS (not supported)Intel HDDL (not supported)Intel GPUIntel Multi-GPUIntel GPU 0Intel GPU 1Intel GPU 2Intel GPU 3Huawei NPUDetection neural network








PersonSelect the detection neural network from the list. By default, the Person detection neural network is selected. 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)VehiclePerson and vehicle (Nano)Person and vehicle (Medium)Person and vehicle (Large)Neural network filter
Neural filter

Yes

By default, the parameter is disabled. To sort out parts of tracks, select the Yes value.

For example:

The Neural tracker detects all freight trucks, and the Neural filter sorts out only the tracks that contain trucks with cargo doors open

NoNeural filter file 

Select a neural network file. You must place the neural network file locally, that is, on the same server where you install Axxon One. You cannot specify the network file in Windows OS

Note
titleAttention!
  • Starting with Detector Pack 3.12, the neural network file of the neural filter must match the processor type specified in the Neural tracker mode parameter.
  • If you use a standard neural network (training wasn't performed in operating conditions), we guarantee an overall accuracy of 80-95% and a percentage of false positives of 5-20%. The standard neural networks are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directoryof 5-20%. The standard neural networks are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directory.

Starting with Detector Pack 3.14, you can add the DISABLE_CALC_HSV system variable to determine the object's color (see Appendix 9. Creating system variable). You can set the following values for the variable:

  • 0—color detection is enabled. The system will collect data about the object's color. This data is necessary for further search in the archive by color.
  • 1—color detection is disabled. Disabling color determination reduces the load on the CPU, including when the detector runs on GPU.

By default, the entire frame is a detection area. If necessary, you can set detection areas (see Configuring a detection area).

Info
titleNote
  • For convenience of configuration, you can "freeze" the frame. Click the Image Removed Image Added button. To cancel the action, click this button again.
  • The detection area is displayed by default. To hide it, click the Image Removed Image Added button. To cancel the action, click this button again.

To save the parameters of the detector, click the Apply Image RemovedImage Added button. To cancel the changes, click the Cancel Image RemovedImage Added button.

Configuring the Neural tracker is complete. If necessary, you can create and configure the necessary sub-detectors on the basis of the neural tracker (see Standard sub-detectors).

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