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Motion Detection (CPU

)

Base motion detection tool

when using the СPU resources. Changing the frame rate in the settings of the detection tool

(

the Frames processed per second parameter)

does not significantly affect the load

Base motion detection tool when using the GPU resources

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

(

the Frames processed per second parameter)

does not significantly affect the load.

The models and the number of GPUs are selected separately using the information in GPU performance for Axxon One detection tools

Service detection tools with decoding by key frames when using the СPU resources:

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

• .

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

Scene analytics detection

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

The results are given for the object tracker with

one active

Motion In Area detection sub-tool

Scene analytics detection

tools (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 detection sub-tool

Scene analytics detection tools (VMDA) based on object tracker using a neural filter and GPU resources. 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 detection sub-tool.

The models and the number of GPUs are selected separately using the information in GPU performance for Axxon One detection tools

Scene analytics detection tools

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 detection tool: 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 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

neurotracker with one active Motion In Area detection sub-tool

Scene analytics detection tools

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 detection tool: 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 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

neurotracker with one active Motion In Area detection sub-tool

Scene analytics detection tools

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 detection tool:

• 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 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

neurotracker with one active Line Crossing detection sub-tool

Scene analytics detection tools

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 detection tool:

• 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

neurotracker with one active Line Crossing detection sub-tool

License plate recognition VT detection tool when using the СPU resources

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

Face detection tool when using СPU resources

Face detection tool

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

Fire detection tool (CPU, 0.

1 FPS)

Smoke detection tool (CPU, 0.

1 FPS)

Fire and smoke detection tools based on neural

network using CPU resources.

The frame rate specified during the detection tool 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

Visitors counter (CPU)

Pose detection tools based on neural

network using CPU resources.

The frame rate specified during the detection tool 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 number of specific pose detection tools created

in the configuration for the

Pose detection parent object does not affect the calculation results (except for the Close-standing people

detection

)

Pose detection tools based on neural

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 (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 number of specific pose detection tools created

in the configuration for the Pose detection parent object does not affect the calculation results (except for the Close-standing people

detection

).

The models and the number of GPUs are selected separately using the information in GPU performance for Axxon One detection tools.

The results are given for

standard neural network

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 detection (CPU,

1 FPS)

Personal protection equipment (PPE) detection tools based on neural

network using CPU resources. 

The frame rate specified during the detection tool 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 detection tool with

five classification

networks operating simultaneously when determining 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 the presence of the necessary equipment

Personal protection equipment (PPE) detection tools based on neural

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 (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 detection tool with

five classification

networks operating simultaneously when determining 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 the presence of the necessary equipment.

The models and the number of GPUs are selected separately using the information in GPU performance for Axxon One detection tools

.
If you use

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