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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.

 In The

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

Changing the frame rate

specified during the detection tool configuration

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.

The models and the number of GPUs are selected separately

using the information on the GPU performance for Axxon Next detection tools page.

Service Detection (CPU,
Motion Detection (The detection tool is applicable only for H.264, H.265 codecs. The platform is calculated for
key frames)
Base motion detection tool with the Decode key frames option enabled. 

Service detectors with decoding by key frames

if the GOP=25 (every 25th frame is the key frame).Service Detection (key frames)Service detection tools

when using the СPU resources:

  • 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 
with a neural filter (CPU)

Scene analytics

detection tools 

detectors (VMDA) based on object tracker

with use of

using a neural filter and CPU resources. 

The results are given for

a tracker with 

the object tracker with a neural filter and with

1

one active

sub detection tool Motion in area.

Motion In Area sub-detector

AI tracker
with 
with a neural filter (GPU)

Scene analytics

detection tools 

detectors (VMDA) based on object tracker

with use of

using a neural filter and GPU resources.

 In

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

The results are given for

a tracker with neural

the object tracker with a neural filter with

1

one active

sub detection tool Motion in area

Motion In Area sub-detector.

The models and the number of GPUs are selected separately

using the information on the GPU performance for Axxon Next detection tools page.

Neurotracker
AI Neural tracker 
(CPU,
6fps
6 FPS)

Scene analytics

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

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

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.

The results are given for

a standard size neural network*.

neurotracker with one active Motion In Area sub-detector

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

Scene analytics

detection tools based on neural tracker with use of VPU resources. 

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

The GPU

In this case, the CPU

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. 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.

The

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

results are given for

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

Scene analytics detection tools based on neural tracker with use of GPU resources. In this case, the GPU decoder operation mode was used.

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

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

.

The results are given for

a standard size neural network*.

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

Neurotracker (GPU, 6 FPS)—Person and Vehicle

Scene analytics detectors based on neurotracker using GPU resources and

AI Neural tracker, enhanced accuracy (GPU, 6fps)Scene analytics detection tools based on neural tracker with use of 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

 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

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

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

Neural counter (GPU, 1FPS)Scene analytics detector based on Neural counter when using the GPU resources. The GPU decoder operation mode was used
The results are given for a standard size neural network*
.
The results are given for
a neural tracker with 1 active sub detection tool Motion in area
the Neural counter with one active Motion In Area sub-detector.
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. 
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
(VT) detection tool.
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

detection tool.

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

The 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)

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

. In this case, the

CPU

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 number of specific human 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).

The models and the number of

VPUs

GPUs are selected separately

using the information on the VPU performance for Axxon Next detection tools page

.

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. In

network using resources of computer vision processor (GPU). In this case, the

CPU

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

detector identifies the presence of the necessary equipment. 

The models and the number of

VPUs

GPUs are selected separately

using the information on the VPU performance for Axxon Next detection tools page

.
If you use

VPU, 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

...

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.