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1. To record mask (highlighting of recognized objects) to archive, select Yes for the corresponding parameter (1).
   
2. If a camera supports multistreaming, select the stream to apply the detection tool to (2). 
3. Select a processing resource for decoding video streams (3). When you select a GPU, a stand-alone graphics card takes priority (when decoding with NVidia NVDEC chips). If there's no appropriate GPU, the decoding will use the Intel Quick Sync Video technology. Otherwise, CPU resources will be used for decoding.
4. If you need to outline objects in the preview window, select Yes in the Detected Objects parameter (4).
5. 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.

6. Set the frame rate value for the detection tool to process (6). This value should be in the range [0.016, 100]. 

7. Set the minimum number of frames with excessive numbers of objects for Neuralcounter to trigger (10). The value should be within the range of 2 – 20.

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

   
   

8. Select the processor for the neural network - CPU, one of GPUs, or Intel NCS (7, see Hardware requirements for neural analytics operation). 

   Note
   title Attention!
   It may take several minutes to launch the algorithm on an NVIDIA GPU after you apply the settings. You can use caching to speed up future launches (see Configuring the acceleration of GPU-based neuroanalytics).

   
   

   Note
   title Attention!
   If you specify other processing resource than the CPU, this device will carry the most of computing load. However, the detection tool will consume CPU as well.

   
   

9. Select the neural network file (8).

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

   
   

10. Set the triggering condition for the neural counter:

    1. In the Number of alarm objects field, set the threshold value for the number of objects in FoV (9). 

    2. In the Trigger upon count field, select the condition polarity: whether triggering should occur on exceeding the threshold, or dropping below it (11). 

11. 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.
    
12. Click Apply.

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