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

The Base tab

Name

Description
Features of calculation

Motion Detection (CPU

, 20fps

)

Base motion

detection toolThe 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:

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

Service detection tools:

  • Quality degradation
  • Blurred Image Detection
  • Compression Artifacts Detection
  • Image Noise Detection
    • 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 for

    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
    Features of calculation
    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 (
    GPU
    CPU)

    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.

    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 neural filter (GPU)

    Scene analytics detectors (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 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 network5AI 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

    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

    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

    neurotracker with one active Line Crossing sub-detector

    Neural counter (GPU, 1FPS
    AI Neural tracker, enchanced accuracy (GPU, 6fps
    )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

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

    LPR&Traffic tab

    Name

    		Description
    Features of calculation

    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 (
    VT
    CPU)License plate recognition
    (VT) detection tool-
    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
    Features of calculation
    Face detection tool

    Face detection tool

    -
    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 Fire&Smoke tab

    Name

    		Description
    Features of calculation

    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
    Features of calculation
    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 CPU resources.

    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 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).

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

    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 distributionEquipment 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

    detector identifies the presence of the necessary equipment.

    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.

    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

     

    The models and the number of GPUs are selected separately.
    If you use 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. 

    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.

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

    ...