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The following Axxon PSIM x64 and vertical solutions detection tools grouped by tabs are available for selection.

Base detection tools

Name

Description
Motion detection (CPU)Base detection tool (activated when the camera is armed) for decoding by all frames and using of CPU resources. The results are given for registry key DetectionFps = 25Changing the value of the DetectionFps key does not significantly affect the load. 

Motion detection (CPU, key frames)

Base detection tool (activated when the camera is armed) for decoding by key frames and using of CPU resources. Changing the frame rate in the detector settings (the Frames processed per second parameter) does not significantly affect the load. 

Service detector (CPU, key frames)

Service detection tools for decoding by key frames and use the СPU resources:

  • Focus detection tool.
  • Stability detection tool.
  • Background change detection tool.
  • Blind detection tool.
  • Cover detection tool.

The platform is calculated for one service detection tool (any of the listed).

The results are given for decoding by key frames if the GOP=25 (every 25th frame is the key frame). The detection tool is applicable only for H.264, H.265 codecs. 

Tracker

Name

Description
Tracker VMDA (CPU)

VMDA detection tools based on object trajectory tracker when using the СPU resources.

AI Tracker with neural filter (GPU)

VMDA detection tools based on object tracker with use of a neural filter and GPU resources. In this case, the CPU decoder operation mode was used.

The models and the number of GPUs are selected separately using the information on the GPU performance for Axxon PSIM detection tools page.

AI Neural tracker (CPU, 6fps)

VMDA detection tools based on neural tracker as part of Axxon PSIM Detector Pack with use of CPU resources.

The frame rate specified during the Neurotracker module configuration (the Frames processed per second parameter) is indicated in brackets. This is the number of fps processed by the module; the frame rate of the incoming video stream is usually higher.

The results are given for a standard size neural network*.

AI Neural tracker (VPU, 6fps)

VMDA detection tools based on neural tracker as part of Axxon PSIM Detector Pack with use of VPU resources. In this case, the CPU decoder operation mode was used.

The frame rate specified during the Neurotracker module configuration (the Frames processed per second parameter) is indicated in brackets. This is the number of fps processed by the module; the frame rate of the incoming video stream is usually higher.

The models and the number of VPUs are selected separately using the information on the VPU performance for Axxon PSIM detection tools page.

The results are given for a standard size neural network*.

LPR&Traffic

Name

Description
License plate recognition VT (CPU)License plate recognition module VIT as part of Auto Axxon PSIM software package when using the СPU resources.
License plate recognition IV (CPU)License plate recognition module LPR IntelliVision as part of Auto Axxon PSIM software package when using the СPU resources.
Vehicle detection IV (CPU)

Traffic flows information gathering subsystem based on IntelliVision vehicle detection as part of Auto Axxon PSIM software package when using the СPU resources.

Face

Name

Description
Facial recognition TVN (CPU)

Face detection tool based on the Tevian recognition module as part of Face Axxon PSIM software package when using the СPU resources.

When calculating the platform using this detection tool, only the resources for face detection and their vectorization are taken into account. The load from comparing faces with the reference database is not taken into account, because usually a separate server is provided to perform this function.

Railway

Name

Description
Wagon number recognition ITB, passenger (CPU)

Recognizer of railway passenger carriage numbers based on the IntLab module as part of Auto Axxon PSIM software package when using the СPU resources.

  1. Optimal resolution is 704*288 OR 640*360 and fps=25. With a higher resolution, the module will not be able to process all frames, which will reduce the quality of operation.
  2. Main channel has the same resource consumption as subordinate channel.
Wagon number recognition ITB, freight (CPU)Recognizer of railway freight carriage numbers based on the IntLab module as part of Auto Axxon PSIM software package when using the СPU resources.
  1. Optimal resolution is 704*288 OR 640*360 and fps=25. With a higher resolution, the module will not be able to process all frames, which will reduce the quality of operation.
  2. Main channel has the same resources consumption as subordinate channel.

Behavior analytics

Name

Description

People counter (CPU)

People counter detection tool that is a part of Axxon PSIM Detector Pack when using the СPU resources.

Optimal parameters for this detection tool are resolution of 800x600 / 640x360 / 640x480 / 320x240 and FPS in range from 24 to 30. If specified parameters of the video stream do not meet these conditions, then when you select this detection tool the resolution is set to 320x240 and FPS=24.

Heat map (CPU)Heat map detection tool (detection of "cold/hot" zones of a store) that is a part of Axxon PSIM Detector Pack when using the СPU resources. The results are given when frame rate in the detector settings (the Frames processed per second parameter) is 25.
Queue length (CPU)Queue length detection tool that is a part of Axxon PSIM Detector Pack when using the СPU resources. The results are given when frame rate in the detector settings (the Frames processed per second parameter) is 25.

Fire&Smoke

Name

Description

Fire detection tool (CPU, 0.1fps)

Smoke detection tool (CPU, 0.1fps)

Fire and smoke detection tools based on neural network as part of Axxon PSIM Detector Pack with use of CPU resources.

The frame rate specified during the detection tool configuration (the Number of frames for analysis and output parameter) is indicated in brackets. This is the number of fps processed by the module; the frame rate of the incoming video stream is usually higher.


* – The results are given for a neural network capable of detecting an object sized at least 5% of the frame width/height. The results may differ for a neural network capable of detecting smaller objects (since more resources are required).

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