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1. If a camera supports multistreaming, select the stream to apply the detection tool to (1). 
   
2. If you need to outline objects in the preview window, select Yes in the Detected Objects parameter (2).
3. Set the recognition threshold for objects in percent (3). If the recognition probability falls below the specified value, the data will be ignored. The higher the value, the higher the accuracy — for the cost of sensitivity.

4. Select the neural network file (4).

   Info
   title Note
   For correct neural network operation under Linux, place the corresponding file in the /opt/AxxonSoft/AxxonNext/ directory.

   
   

5. Set the minimum number of frames with excessive numbers of objects for Neuralcounter to trigger (8). The value should be within the range of 2 – 20.

6. Set the interval between the analyzed frames in seconds (5). The value should be within the range of 0,05 – 30.

   Info
   title Note
   The default values (3 frames and 1 second) indicate that Neuralcounter will analyze one frame every second. If Neuralcounter detects more objects than the specified threshold value on 3 frames, then it triggers.

   
   

7. Set the recognition threshold for objects in percent (5). If the recognition probability falls below the specified value, the data will be ignored. The higher the value, the higher the accuracy — for the cost of sensitivity.
8. Set the detection threshold value for minimum number of objects in zone (7).
9. Select the processor for the neural network - CPU, one of GPUs, or Intel NCS (6, see Hardware requirements for neural analytics operation). 
10. In the preview window, you can set the detection zones with the help of anchor points much like privacy masks in Scene Analytics (see Setting General Zones for Scene Analytics). By default, the entire FoV is a detection zone.
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11. Click Apply.