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ParameterValueDescription
Object features
Video streamMain stream

If the camera supports multistreaming, select the stream for which detection is needed.

Other
EnableYesThe detector is enabled by default. To disable the detector, select the No value
No
NameMeta-detectorEnter the 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 used for decoding

CPU
GPU
HuaweiNPU
Number of frames processed per second
1

Specify the number of frames for the detector to process per second. The value must be in the range [0.016, 100]

TypeMeta-detector

Name of the detector type (non-editable field)

Advanced settings

Number of measurements in a row to trigger detection

15

Set the minimum number of frames, on which the detector must detect a match between a text query and an image in order to generate an event. The value must be in the range [1, 20]

Basic settings

Detection mode


 

 

 

 

 

CPU

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

Detection threshold

20

Specify 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 accuracy, but some events from the detector can be missed. The value must be in the range [0,05, 100]

Text query 1




Enter a text query in English.

For example:

  • Human and dog
  • Running human
  • Fallen human
Note
titleAttention!

Optimal query structure:

  • Object type (human, vehicle, and so on).
  • Object key attribute (color, size, and so on);
  • Surroundings (road, building, room, and so on);
  • Additional scene specifications (angle, position in frame).

Examples: Woman wearing a black dress, White car pedestrian crossing top view.

We recommend using chatbots with artificial intelligence of the latest generation, such as ChatGPT-4o, which are able to:

  • Generate optimized queries relying on the syntax of the BLIP-2 model (generate text descriptions from an image).
  • Work with web links.


Text query 2

Text query 3

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