Versions Compared

Old Version 3

changes.mady.by.user Gleb Matskevich

Saved on

compared with

New Version 4

changes.mady.by.user Alena Kniazeva

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.
Tip

Hardware requirements for neural analytics operation

Video stream and scene requirements for the Stopped object detector

Image requirements for the Stopped object detector

To configure the Stopped object detector, do the following:

...

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 results in a heavy load on the Server and limits the number of cameras that you can use on it.

No
Video streamMain 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
EnableYesBy default, the detection tool is enabled. To disable, select the No value
No
NameStopped object detectorEnter the detection tool name or leave the default name
Decoder modeAuto

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 used for decoding

CPU
GPU
HuaweiNPU
TypeStopped object detectorName of the detection tool type (non-editable field)
Basic settings
Detection threshold30Specify 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 may not be considered. The value must be in the range [0.051, 100]
Neurotracker mode








CPU

Select the a processor for the neural network operation—CPU, one of Nvidia GPUs, or one of Intel GPUs operation (see Hardware requirements for neural analytics operation, Selecting Nvidia GPU when configuring detection tools).

Note
titleAttention!









Nvidia GPU 0
Nvidia GPU 1
Nvidia GPU 2
Nvidia GPU 3
Intel GPU
Intel HDDL (not supported)
Huawei NPU
Object type





Person

Select the recognition object.

  • Nano—low accuracy, low processor load.
  • Medium—medium accuracy, medium processor load.
  • Large—high accuracy, high processor load
Person (top-down 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 time3

Specify the waiting time for the reappearance of a disappeared stopped object in seconds. The value must be in the range [1, 60]

Stop time5

Specify the time in seconds after which the object will be considered stopped. The value must be in the range [1, 60]

Selected object class 

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 (Object type, Neural network file).
    2. If you specify a class/classes from the neural network, the tracks of the specified class/classes will be displayed (Object type, 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 (Object type, 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 will be displayed (Object type, 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 won’t be displayed (Object type, Neural network file).

Camera position

Wall

To eliminate false events from the detection tool when using a fisheye camera, select the correct device location. For other devices, this parameter is irrelevant

Ceiling
Neural 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 the neural network file is not specified, the default file will be used, which is selected automatically depending on the selected object type (Object type) and the selected processor for the neural network operation (Decoder mode). If you use a custom neural network, enter a path to the file. The selected object type 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. 


...