To configure the Stopped object detector, do the following:
- Go to
the Detection Tools tab- the Detectors tab.
Below the required camera,
click Create… → Category: Trackers → Stopped object detector.
By default, the detection tool detectoris enabled and set to detect stopped objects.
If necessary, you can change the detection tool detector 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. | Note |
|---|
| To obtain metadata, video is decompressed and analyzed, which results in a heavy load on the server and limits the number of cameras that you can use on it. |
|
| No |
| Video stream | Main stream | If the camera supports multistreaming, |
select the stream for which detection is needed. Selecting a low-quality video stream reduces the load on the server |
| Second stream |
| Other |
| Enable | Yes | By default, the |
detection tool | detector is enabled. To disable, select the No |
value| value |
| No |
| Name | Stopped object detector | Enter the |
detection tool | detector name or leave the default name |
| 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 NVDEC chips). If there is no appropriate GPU, the decoding will use the Intel Quick Sync Video technology. Otherwise, CPU resources |
will be are used for decoding |
| CPU |
| GPU |
| HuaweiNPU |
| Type | Stopped object detector | Name of the |
detection tool | detector type (non-editable field) |
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, but some events from the |
detection tool | detector may not be considered. The value must be in the range [ |
0.05Neurotracker the a processor for the neural network |
operation—CPU, one of Nvidia GPUs, or one of Intel GPUs detection tools |
| Nvidia GPU 0 |
| Nvidia GPU 1 |
| Nvidia GPU 2 |
| Nvidia GPU 3 |
| Intel GPU |
| Intel Multi-GPU |
| Intel GPU 0 |
| Intel GPU 1 |
| Intel GPU 2 |
| Intel GPU 3 |
| Intel HDDL (not supported) |
| Huawei NPU |
Object typeDetection neural network
| Person | Select the |
recognition object.
- Nano—low accuracy, low processor load.
- Medium—medium accuracy, medium processor load.
- Large—high accuracy, high processor load
detection 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 |
Person (top | view) |
| Person (top-down view Nano) |
| Person (top-down view Medium) |
| Person (top-down view Large) |
| Vehicle |
| Person and vehicle (Nano) |
| Person and vehicle (Medium) |
| Person and vehicle (Large) |
Advanced settings
|
Wait time (sec)
| 3
| Specify the waiting time for the reappearance of a disappeared stopped object in seconds. The value must be in the range [1, 60] |
Stop time (sec)
| 5
| Specify the time in seconds after which the object |
will be is considered stopped. The value must be in the range [1, 60] |
| 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 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 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 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 Object typeDetection neural network, Neural network file)
|
with 3.10.2, if you specify a class/classes missing from the neural network, the tracks |
|
won’t be Object typeDetection neural network, Neural network file). |
|
Camera position
| Wall
|
If you use a fish-eye camera, then To eliminate false events from the detector when using a fisheye camera, select the correct |
location of the device to filter out false events. This parameter is not relevant for other devicesdevice location. For other devices, this parameter is irrelevant |
Ceiling
|
| Neural network file | | If you use a custom neural network, select the corresponding file. | Note |
|---|
| - 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
|
|
is not specified will be , which - that is selected automatically depending on the selected
|
|
object type (Object type) - value in the Detection neural network parameter and the selected processor for the neural network operation
|
|
() If - If you use a custom neural network, enter a path to the file. The selected
|
|
object type - detection neural network is ignored when you use a custom neural network.
|
|
To ensure the correct operation of the neural network - If you use a 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.
- 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
|
|
must be located - locally in the /opt/AxxonSoft/DetectorPack/NeuroSDK directory
|
|
. - or in the network folder with the corresponding access rights.
|
|
By default, the entire frame is a detection area.If necessary, in the preview window, you can set one or more:
...
- area),
- ignore areas (see Configuring the
...
| Info |
|---|
|
- 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 detection tooldetector, click the Apply 
Image RemovedImage Added button. To cancel the changes, click the Cancel 
Image Removed Image Added button.
The Configuring the Stopped object detector is configuredcomplete.
