Documentation for Axxonsoft Platform Calculator. Documentation for other products available here.
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The following Axxon One x64 detection tools grouped by tabs are available for selection.
Base tab
Name | Description | |
|---|---|---|
Motion Detection (CPU) | Base motion detection tool when using the СPU resources. Changing the frame rate in the detector settings (the Frames processed per second parameter) does not significantly affect the load. | |
| Motion Detection (GPU) | 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 detector settings (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 on the GPU performance for Axxon One detection tools page. | |
| Service Detection (key frames) | Service detection tools for decoding by key frames and use the СPU resources:
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. | |
| Detection embedded in camera (CPU) | Embedded detection tools (built-in analytics) in camera when using the СPU resources. | |
Tracker tab
Name | Description | |
|---|---|---|
| Tracker VMDA (CPU) | Scene analytics detection tools (VMDA) based on object tracker when using the СPU resources. The results are given for the object tracker with 1 active sub detection tool Motion in area. | |
| AI tracker with neural filter (CPU) | Scene analytics detection tools (VMDA) based on object tracker with use of a neural filter and CPU resources. The results are given for a tracker with neural filter with 1 active sub detection tool Motion in area. | |
| AI tracker with neural filter (GPU) | Scene analytics detection tools (VMDA) based on object tracker with use of a neural filter and GPU resources. In this case, the CPU decoder operation mode was used. The results are given for a tracker with neural filter with 1 active sub detection tool Motion in area. The models and the number of GPUs are selected separately using the information on the GPU performance for Axxon One detection tools page. | |
| 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 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) | Scene analytics detection tools based on neural tracker with use of VPU resources. In this case, the CPU decoder operation mode was used. 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 models and the number of VPUs are selected separately using the information on the VPU performance for Axxon One 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. 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 models and the number of GPUs are selected separately using the information on the GPU performance for Axxon One 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. | |
| 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. In this case, the GPU decoder operation mode was used. 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 models and the number of GPUs are selected separately using the information on the GPU performance for Axxon One 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. | |
| AI Neural tracker, enhanced accuracy (CPU, 6fps) | Scene analytics detection tools based on neural tracker with use of CPU resources and high-precision neural network. In this case, the CPU decoder operation mode was used. 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 network*. The results are given for a neural tracker with 1 active sub detection tool Motion in area. | |
LPR&Traffic tab
Name | Description | |
|---|---|---|
| License plate recognition VT (CPU) | License plate recognition VT detection tool when using the СPU resources. | |
| License plate recognition RR (CPU) | License plate recognition RR detection tool when using the СPU resources. | |
| License plate recognition RR (GPU) | License plate recognition RR detection tool when using the GPU resources. | |
| Vehicle make and model recognition RR (CPU) | Vehicle vendor, model, type, color and headlights status recognition RR detection tool when using the СPU resources. | |
| Vehicle make and model recognition RR (GPU) | Vehicle vendor, model, type, color and headlights status recognition RR detection tool when using the GPU resources. | |
| License plate, make and model recognition RR (CPU) | License plate recognition with activated Make and model recognition (MMR) RR detection tool when using the СPU resources. | |
| License plate, make and model recognition RR (GPU) | License plate recognition with activated Make and model recognition (MMR) RR detection tool when using the GPU resources. | |
Face tab
Name | Description | |
|---|---|---|
| Facial recognition (CPU) | Face detection tool when using the СPU resources. | |
Fire&Smoke tab
Name | Description | |
|---|---|---|
Fire detection tool (CPU, 0.1fps) Smoke detection tool (CPU, 0.1fps) | Fire and smoke detection tools based on neural network with use of 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. | |
Behavior analytics tab
Name | Description | |
|---|---|---|
People counter (CPU) | Visitor counter when using CPU resources. The results are given when frame rate in the detector settings (the Frames processed per second parameter) is 25. | |
| Heat map (CPU) | Heat map based on object tracker when using the СPU resources. | |
| Queue length (CPU) | Queue detection tool when using the СPU resources. | |
| AI Pose detection (CPU, 3fps) | Pose detection tools based on neural network with use of 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 under the head Pose detection 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). | |
| AI Pose detection (VPU, 3fps) | Pose detection tools based on neural network with use of VPU resources. In this case, the CPU 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 under the head Pose detection 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). The models and the number of VPUs are selected separately using the information on the VPU performance for Axxon One detection tools page. The results are given for the standard neural network included in the Axxon One distribution. | |
| AI Pose detection (GPU, 3fps) | Pose detection tools based on neural network with use of GPU resources. 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 under the head Pose detection 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). The models and the number of GPUs are selected separately using the information on the GPU performance for Axxon One detection tools page. The results are given for the standard neural network included in the Axxon One distribution. | |
Equipment detection (CPU, 1fps) | Personal protection equipment (PPE) detection tools based on neural network with use of 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 5 classification nets 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. | |
| Equipment detection (VPU, 1fps) | Personal protection equipment (PPE) detection tools based on neural network with use of VPU resources. In this case, the CPU 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 5 classification nets 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 VPUs are selected separately using the information on the VPU performance for Axxon One 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. | |
| Equipment detection (GPU, 1fps) | Personal protection equipment (PPE) detection tools based on neural network with use of GPU resources. 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 5 classification nets 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 on the GPU performance for Axxon One detection tools page. | |
* – 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).