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The following Axxon NextOne x64 detection tools detectors grouped by tabs are available for selection.

The Base tab

Name

Description

Motion Detection (CPU

, 20fps

)

Base motion

detection tool.The frame rate specified during the detection tool configuration

detector when using the СPU resources. Changing the frame rate in the settings of the detector (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.

does not significantly affect the load

Motion Detection (GPU
, 20fps
)

Base motion

detection tool

detector when using the GPU resources

.The frame rate specified during the detection tool configuration

. In this case, the GPU decoder operation mode was used. Changing the frame rate in the settings of the detector (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.

1 NVidia Quadro RTX 4000 card, regardless of the codec (H.264, H.265), processes up to 238 channels of 640x360 video with 25 fps; in case of 1920x1080 video with 25 fps, the number of channels depends on the codec: up to 55 channels for H.264, and up to 90 channels for H.265. For details, see GPU performance for Axxon Next detection tools.

Multiple NVidia Quadro RTX 4000 cards can be used on the server.

Motion Detection (key frames)

Base motion detection tool with the Decode key frames option enabled.

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

Service Detection (key frames)

does not significantly affect the load.

The models and the number of GPUs are selected separately

Service Detection (CPU, key frames)

Service detectors with decoding by key frames when using the СPU resources

Service detection tools

:

  • Quality degradation.
  • Blurred Image Detection.
  • Compression Artifacts Detection.
  • Image Noise Detection.
Scene change
  • Scene change.

The platform is calculated for one service

detection tool

detector (any of the listed).

The

detection tool is applicable only for H.264, H.265 codecs. The platform is calculated

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

Detection embedded in camera (CPU)Embedded detectors (built-in analytics) in camera when using the СPU resources

The Tracker tab

Name

Description
Tracker VMDA (CPU)

Scene analytics

detection tools 

detectors (VMDA) based on object tracker when using the СPU resources.

The results are given for the object tracker with

1

one active

sub detection tool Motion in area.

Motion In Area sub-detector

AI tracker with a neural filter (CPU)

Scene analytics detectors (VMDA) based on object tracker using a neural filter and CPU resources. 

The results are given for the object tracker with a neural filter and with one active Motion In Area sub-detector

AI tracker with a
AI tracker with 
neural filter (GPU)

Scene analytics

detection tools 

detectors (VMDA) based on object tracker

with use of

using a neural filter and GPU resources.

For each track, one image per second is sent to neural network for classification.

  • The NVIDIA GeForce GT 730 graphics card is capable of processing up to 701 classifications2 per second.

  • The NVIDIA GeForce GTX 1070 graphics card is capable of processing up to 2203 classifications per second.

  • The Intel Neural Compute Stick 1 (movidius I) is capable of processing up to 58 classifications per second4.

  • The Intel Neural Compute Stick 2 (movidius II) is capable of processing up to 200 classifications per second4.

  • Several video cards can be used in one system.
  • For example, if you need to track 9 persons per second on 10 cameras, then a GeForce GTX 1070 or similar video card is suitable.
  • Up to two Intel Neural Compute Stick can be used in one system.

In this case, the CPU decoder operation mode was used.

The results are given for the object tracker with a neural filter with one active Motion In Area sub-detector.

The models and the number of GPUs are selected separately

Neurotracker (CPU, 6 FPS)

Scene analytics detectors based on neurotracker using CPU resources and resource-intensive neural networks to detect people or vehicles.

You can select the type of recognition object for the detector: Person, Person (top-down view), Vehicle.

Relative accuracy: medium. Relative resource intensity: low.

These neural networks are embedded in the product and can be trained on demand to detect different objects. The frame rate specified during the Neurotracker object

AI Neural tracker (CPU, 6fps)

Scene analytics detection tools based on neural tracker with use of CPU resources.

The frame rate specified during the Neurotracker object

configuration (the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

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 network5.

neurotracker with one active Motion In Area sub-detector

Neurotracker (GPU, 6 FPS
AI Neural tracker (VPU, 6fps
)

Scene analytics

detection tools

detectors based on neurotracker using GPU resources and resource-intensive neural networks to detect people or vehicles.

The GPU decoder operation mode was used.

You can select the type of recognition object for the detector: Person, Person (top-down view), Vehicle.

Relative accuracy: medium. Relative resource intensity: low.

These neural networks are embedded in the product and can be trained on demand to detect different objects.

 based on neural tracker with use of VPU resources.

The frame rate specified during

the

the Neurotracker

object

 object configuration (the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher

.1 Mustang-V100-MX8 (Intel HDDL) card processes up to 60 video channels regardless of video resolution

.

Multiple Mustang-V100-MX8 (Intel HDDL) cards can be used on the server.

The results are given for

a standard size neural network5.AI Neural tracker (GPU, 6fps)

Scene analytics detection tools based on neural tracker with use of GPU resources.

neurotracker with one active Motion In Area sub-detector

Neurotracker (CPU, 6 FPS)—Person and Vehicle

Scene analytics detectors based on neurotracker using CPU resources and high-precision neural network to detect people and (or) vehicles.

You can select the type of recognition object and accuracy for the detector:

  • Nano: relative accuracy—moderately high, relative resource intensity—medium.
  • Medium: relative accuracy—high, relative resource intensity—high.

These neural networks are embedded in the product and can be trained on demand to detect different objects. The frame rate specified during

the

the Neurotracker

object

 object configuration (the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher.

1 NVidia Quadro RTX 4000 card, regardless of the codec (H.264, H.265), processes up to 73 channels of 640x360 video with 25 fps; in case of 1920x1080 video with 25 fps, the number of channels depends on the codec: up to 52 channels for H.264, and up to 73 channels for H.265. For details, see GPU performance for Axxon Next detection tools.

Multiple NVidia Quadro RTX 4000 cards can be used on the server.

The results are given for neurotracker with one active Line Crossing sub-detector

Neurotracker (GPU, 6 FPS)—Person and Vehicle

Scene analytics detectors based on neurotracker using GPU resources and high-precision neural network to detect people and (or) vehicles. In this case, the GPU decoder operation mode was used.

You can select the type of recognition object and accuracy for the detector:

  • Nano: relative accuracy—moderately high, relative resource intensity—medium.
  • Medium: relative accuracy—high, relative resource intensity—high.
  • Large: relative accuracy—very high, relative resource intensity—very high.

These neural networks are embedded in the product and can be trained on demand to detect different objects. The frame rate specified during the Neurotracker object 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 network5

.

The results are given for

a neural tracker with 1 active sub detection tool Motion in area.

neurotracker with one active Line Crossing sub-detector

Neural counter
AI Neural tracker, enhanced accuracy 
(GPU,
6fps
1FPS)Scene analytics
detection tools based on neural tracker with use of GPU resources and high-precision neural network
detector based on Neural counter when using the GPU resources. The GPU decoder operation mode was used.
The results are given for the Neural counter with one active Motion In Area sub-detector.
The frame rate specified during the
Neurotracker object
detector configuration (
the 
the Frames processed per second parameter) is indicated in brackets.
 This
This is the number of
fps
FPS processed by the module; the frame rate of the incoming video stream is usually higher.

1 NVidia Quadro RTX 4000 card, regardless of the codec (H.264, H.265), processes up to 61 channels of 640x360 video with 25 fps; in case of 1920x1080 video with 25 fps, the number of channels depends on the codec: up to 52 channels for H.264, and up to 61 channels for H.265. For details, see GPU performance for Axxon Next detection tools

Multiple NVidia Quadro RTX 4000 cards can be used on the server.

The results are given for a standard size neural network5.

The results are given for a neural tracker with 1 active sub detection tool Motion in area.

LPR&Traffic tab

...

License plate recognition (VT) detection tool.

 
You can select the type of recognition object for the detector: Person, Person (top-down view), Vehicle.

LPR&Traffic tab

Name

Description
License plate recognition VT (CPU)

License plate recognition VT detector when using the СPU resources

License plate recognition RR (CPU)License plate recognition RR detector when using the СPU resources
License plate recognition RR (GPU)License plate recognition RR detector when using the GPU resources
Vehicle make and model recognition RR (CPU)

Detector recognizes makes, models, type, color and running lights of RR vehicles when using СPU resources

Vehicle make and model recognition RR (GPU)Detector recognizes makes, models, type, color and running lights of RR vehicles when using СPU resources
License plate, make and model recognition RR (CPU)License plate recognition RR with enabled Make and model recognition (MMR) detector when using СPU resources
License plate, make and model recognition RR (GPU)License plate recognition RR with enabled Make and model recognition (MMR) detector when using GPU resources
License plate recognition IV (CPU)License plate recognition IV detector when using СPU resources
License plate recognition IV (GPU)License plate recognition IV detector when using GPU resources

The Face tab

Name

Description
Facial recognition (CPU)

Face detector when using СPU resources

Facial recognition VA (GPU)

Face detector when using GPU resources. The GPU decoder operation mode was used

The

Face tab

...

Face detection tool.

Fire&Smoke tab

Name

Description

Fire

detection tool

detector (CPU, 0.

1fps

1 FPS)

Smoke

detection tool

detector (CPU, 0.

1fps

1 FPS)

Fire and smoke

detection tools

detectors based on neural

network with use of

network using CPU resources.

The frame rate specified during the

detection tool configuration

detector configuration (

the 

the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher

Fire detector (GPU, 0.1 FPS)

Smoke detector (GPU, 0.1 FPS)

Fire and smoke detectors based on neural network using GPU resources.
The frame rate specified during the detector 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 Behavior analytics tab

Name

Description

AI Pose detection (CPU, 3fps)

Visitors counter (CPU)

Visitors counter when using CPU resources. The results are given when frame rate in the settings of the detector (the Frames processed per second parameter) is 25
Heat map (CPU)Heat map based on object tracker when using СPU resources
Queue detector (CPU)Queue detector when using СPU resources
Human pose detector (CPU, 3 FPS)

Human pose detectors based on neural network using

Pose detection tools based on neural network with use of

CPU resources.

The frame rate specified during the

detection tool configuration

detector configuration (

the 

the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher.

The number of specific pose

detection tools created under the head Pose detection

detectors created in the configuration for the Human pose detector parent object does not affect the calculation results (except for the Close-standing people

detection; to calculate the result with this detection tool, please contact the AxxonSoft support).

detector)

Human pose detector (GPU, 3 FPS)

Human pose detectors based on neural network using resources of computer vision processor (GPU). In this case, the GPU decoder operation mode was used

AI Pose detection (VPU, 3fps)Pose detection tools based on neural network with use of VPU resources

.

The frame rate specified during the

detection tool configuration

detector configuration (

the 

the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher.

The number of specific human pose

detection tools created under the head Pose detection

1 Mustang-V100-MX8 (Intel HDDL) card processes up to 28 channels regardless of video resolution.

Multiple Mustang-V100-MX8 (Intel HDDL) cards can be used on the server.

detectors created in the configuration for the Human pose detector parent object does not affect the calculation results (except for the Close-standing people

detection; to calculate the result with this detection tool, please contact the AxxonSoft support).

detector).

The models and the number of GPUs are selected separately .

The results are given for

the

standard neural network

included in the Axxon Next distribution.Equipment detection (CPU, 1fps

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

Equipment detector (CPU, 1 FPS)

Personal protection equipment (PPE)

detection tools

detectors based on neural

network with use of

network using CPU resources. 

The frame rate specified during the

detection tool configuration

detector configuration (

the 

the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher.

The results are given for a

detection tool

detector with

5

five classification

nets

networks operating simultaneously when

determining

identifying equipment on each body part (head, torso, hands, legs, feet) in a gateway: at the entrance to the area in which the equipment is required, an employee lingers for 5-10 seconds during which the

detection tool determines

detector identifies the presence of the necessary equipment

.

Equipment
detection
detector (
VPU
GPU,
1fps
1 FPS)

Personal protection equipment (PPE)

detection tools

detectors based on neural

network with use of VPU resources

network using resources of computer vision processor (GPU). In this case, the GPU decoder operation mode was used.

The frame rate specified during the

detection tool configuration

detector configuration (

the 

the Frames processed per second parameter) is indicated in brackets.

 This

This is the number of

fps

FPS processed by the module; the frame rate of the incoming video stream is usually higher.

The results are given for a

detection tool

detector with

5

five classification

nets

networks operating simultaneously when

determining

identifying equipment on each body part (head, torso, hands, legs, feet) in a gateway: at the entrance to the area in which the equipment is required, an employee lingers for 5-10 seconds during which the

detection tool determines

1 Mustang-V100-MX8 (Intel HDDL) card processes up to 40 channels regardless of video resolution.

Multiple Mustang-V100-MX8 (Intel HDDL) cards can be used on the server

detector identifies the presence of the necessary equipment.

 

The models and the number of GPUs are selected separately

.
If you use

Mustang-V100-MX8 (Intel HDDL), please note that due to the peculiarities of the device, only the segmentation neural network will be processed on it, and the CPU will be involved in the operation of the classification neural networks.

...

titleNote

1 – The results are given for the Core i5-3570 (3400 MHz) CPU and may vary depending on the CPU installed. For example, the Xeon Gold 6140 (2300 MHz) CPU allows 95 classifications2 per second.

2 – 1 classification per second is 1 object detected on video. For example, if average of 9 moving objects are simultaneously present on video from one camera, and there are 5 cameras in the system, then you need to use a video card allowing 45 classifications per second.

3 – The results are given for the Core i7-8700 (3200 MHz) CPU and may vary depending on the CPU installed.

GPU, both segmenting neural network and classification neural networks are processed on it

Meta-detector (GPU)Meta-detector based on neural network when using GPU resources. The results are given when frame rate in the settings of the detector (the Frames processed per second parameter) is 1. The frame rate specified during the detector configuration (the Frames processed per second parameter) is the number of FPS processed by the module; the frame rate of the incoming video stream is usually higher. 
Crowd esimation VA (GPU)Crowd estimation detector based on neural network using GPU resources. The results are given when frame rate in the settings of the detector (the Frames processed per second parameter) is 0.017. The frame rate specified during the detector configuration (the Frames processed per second parameter) is the number of FPS processed by the module; the frame rate of the incoming video stream is usually higher. 

4 – The results are given for the Core i7-3770 (3400 MHz) CPU and may vary depending on the CPU installed.

...