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Configuring the detection tool
To configure the Scene Analytics detection tools based on Neurotracker Neural tracker, do the following:
- Go to
the - the Detection Tools
tab- tab.
Below the required camera,
click Create… → Category: Trackers →
Neural tracker.
By default, the detection tool is enabled and set to detect moving people.
If necessary, you can change the settings of the detection tool parameters. The list of parameters is given in the table:
| Parameter | Value | Description |
|---|
| Object features |
| Record objects tracking | Yes | By default, metadata are recorded into the database. To disable metadata recording, select the No value |
| No |
| Video stream | Main stream | If the camera supports multistreaming, |
select select the stream for which detection is needed |
| Second stream |
| Other |
| Enable | Yes | By default, the detection tool is enabled. To disable, select |
the valueNeurotracker| Neural tracker | Enter the detection tool name or leave the default name |
| Decode key frames | Yes | By default, the Decode key frames parameter is disabled. Using this option reduces the load on the Server, but at the same time the quality of detection is reduced. To decode only the key frames, select the Yes value. We recommend enabling this parameter for "blind" (without video image display) Servers on which you want to perform detection. For MJPEG codec decoding isn’t relevant, as each frame is considered a key frame. | Note |
|---|
| The Number of frames processed per second and Decode key frames parameters are interconnected. If there is no local Client connected to the Server, the following rules work for remote Clients: - If the key frame rate is less than the value specified in the Number of frames processed per second field, the detection tool will work by key frames.
- If the key frame rate is greater than the value specified in the Number of frames processed per second field, the detection will be performed according to the set period.
If a local Client connects to the Server, the detection tool will always work according to the set period. After a local Client disconnects, the above rules will be relevant again. |
|
No |
| 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 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 |
Neurofilter General information on configuring detection). NVIDIA |
| Nvidia GPU 0 |
| Nvidia GPU 1 |
| Nvidia GPU 2 |
| Nvidia GPU 3 |
| Intel NCS (not supported) |
| Intel HDDL (not supported) |
| Intel GPU |
| Huawei NPU |
| Number of frames processed per second | 6 | 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 ; , 100]. | Note |
|---|
| We recommend the value of at least 6 FPS |
|
or more is recommended. For fast moving objects (running individuals, vehicles), you must set the frame rate at 12 FPS or above |
|
(see Examples of configuring Neurotracker for solving typical tasks)Neurotracker| Neural tracker | Name of the detection tool type (non-editable field) |
Advanced settings
|
Camera position
| Wall | To |
eliminate false positives | sort out false events from the detection tool when using a fisheye camera, |
select the correct device location. For other devices, this parameter is irrelevant
|
| Ceiling |
Hide moving objects
| Yes | By default, the parameter is 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 |
|---|
| If a static object starts moving, the detection tool will create a track, and the object will no longer be considered static. |
|
| No |
Hide static objects
| Yes | By default, the parameter is disabled. If you don't need to detect static objects, select the Yes value. This parameter lowers the number false |
positives events from the detection tool when detecting moving objects. An object is considered static if it has not moved more than 10% of its width or height during the whole time of its track existence. | Note |
|---|
| If a static object starts moving, the detection tool will |
|
triggercreate a track, and the object will no longer be considered static. |
|
| No |
| Minimum number of detection triggers | 6 | Specify the Minimum number of detection triggers for the |
neurotracker | Neural tracker to display the object's track. The higher the value, the |
more is | longer the time interval between the detection of an object and the display of its track on the screen. Low values of this parameter |
may positives| events from the detection tool. The value must be in the range [2, 100] |
Model quantization
| Yes |
To quantize the network, select the Yes value. This parameter By default, the parameter is disabled. The parameter is applicable only to standard neural networks for Nvidia GPU. It allows you to reduce the consumption of |
the GPU processing power.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 |
|---|
| 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% |
|
.Model quantization is only applicable to NVIDIA GPUs. - The first launch of a detection tool with the Model quantization parameter enabled
|
|
may - can take longer than a standard launch.
|
|
If is a - the detection tool with quantization will run without delay.
|
|
| No |
| Neural network file | | |
unique custom neural network, select the corresponding file |
.| Note |
|---|
| - To train your neural network, contact AxxonSoft (
|
|
see - 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 the neural network file is not specified, the default file
|
|
will be , which - that is selected automatically depending on the selected
|
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object type (Object type) - value in the Detection neural network parameter and the selected processor for the neural network operation
|
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() If - If you use a custom neural network, enter a path to the file. The selected
|
|
object type o - detection neural network is ignored when you use a custom neural network.
- 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 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.
|
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Scanning window
| Yes |
T| By default, the parameter is disabled. To enable the scanning mode, select the Yes value (see |
Scanning mode in Axxon One 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 detection tool will not operate with such settings. |
|
| 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 |
| Selected object |
class| classes | | If necessary, specify the class of the detected object. |
If 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 will be displayed (
|
Object type- Detection neural network, Neural network file).
- If you specify a class/classes from the neural network, the tracks of the specified class/classes will be displayed (
|
Object type- 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 will be displayed (
|
Object type- 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 will be displayed (
|
Object typeDetection neural network, Neural network file)
|
. with 3 3.10.2, if you specify a class/classes missing from the neural network, the tracks won’t be displayed ( |
|
Object typeDetection neural network, Neural network file). |
|
Similitude search
| Yes | By default, the parameter is disabled. To enable the search for similar persons, select |
the Yes the Yes value. If you enabled the parameter, it increases |
the processor loadThe works works only on tracks of people. |
|
| No |
| Time of processing similitude track (sec) | 0 | Specify the time in |
the range [0; 3600] required 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 objects | 0 | Specify 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 ; | , 86 400] |
| Track retention time | 0.7 | Specify the time in seconds after which the object track is considered lost. This helps if objects in scene temporarily overlap each other. For example, when a larger vehicle |
may block | blocks the smaller one from view. The value must be in the |
range | range [0.3, 1000] |
Basic settings
|
| Detection threshold | 30 | Specify the Detection threshold for objects in percent. |
If | If the recognition probability falls below the specified value, the data will be ignored. The higher the value, the higher the |
accuracy| detection quality, but some |
triggers | events from the detection tool may not be considered. The value must be in the range [0.05, 100] |
Neurotracker General information on configuring detection NVIDIA neurotracker - neural tracker is running on GPU, object tracks
|
|
may - can be lagging behind the objects in the
|
|
Surveillance - Surveillance window. If this happens, set the camera buffer size to 1000 milliseconds (
|
|
see The object- ).
- Starting with Detector Pack 3.11, Intel HDDL and Intel NCS aren’t supported.
|
|
| Nvidia GPU 0 |
| Nvidia GPU 1 |
| Nvidia GPU 2 |
| Nvidia GPU 3 |
| Intel |
NCS | NCS (not supported) |
| Intel HDDL (not supported) |
| Intel GPU |
| Huawei NPU |
Object typeDetection neural network
| Person | Select the |
recognition objectdetection 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) |
—low accuracy, low processor load |
| Person and vehicle (Medium) |
—medium accuracy, medium processor load |
| Person and vehicle (Large) |
—high accuracy, high processor loadNeurofilterTo use the neurofilter to sort out certain By default, the parameter is disabled. To sort out parts of tracks, select the Yes value. For example |
, the neurotracker : Neural tracker detects all freight trucks, and the |
neurofilter Neural filter sorts out only the tracks that contain trucks with cargo door open |
| No |
Neurofilter | Neural filter file | | Select a neural network file | Note |
|---|
| 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. |
|
By default, the entire frame is a detection area. If necessary, in the preview window, set detection areas with the help of anchor points points 
(the same as with the excluded areas of the Scene analytics detection tools, see Setting General Zones for Scene analytics detection tools). By default, the whole frame is see Configuring a detection area).
| Info |
|---|
|
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. |
To save the parameters of the detection tool, click the Apply 
Image Modified button. To cancel the changes, click the Cancel 
Image Modified button.
The next step is to If necessary, you can create and configure the necessary detection tools sub-detectors on the basis of neurotracker. The configuration procedure is the same as for the basic Neural tracker (see Setting up Tracker-based Scene Analytics detection tools Standard sub-detectors).
| Note |
|---|
|
To trigger get an event from the Motion in Area detection tool area sub-detector on the basis of neurotracker Neural tracker, an object must be displaced by at least 25% of its width or height in FOV.The abandoned objects detection tool works only with the basic object tracker.the frame. |
Example of configuring Neural tracker for solving typical tasks
| Parameter | Task: detection of moving people | Task: detection of moving vehicles |
|---|
| Other |
| Number of frames processed per second | 6 | 12 |
Neural network filter
|
| Neural filter | No | No |
Basic settings
|
| Detection threshold | 30 | 30 |
Advanced settings
|
| Minimum number of detection triggers | 6 | 6 |
| Camera position | Wall | Wall |
| Hide static objects | Yes | Yes |
| 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 | 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 |
