Documentation for Axxonsoft Platform Calculator. Documentation for other products available here.
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The following Axxon One x64 detectors grouped by tabs are available for selection.
The Base tab
Name | Description | |
|---|---|---|
Motion Detection (CPU) | Base motion detector when using the СPU resources. Changing the frame rate in the settings of the detector (the Frames processed per second parameter) does not significantly affect the load | |
| Motion Detection (GPU) | Base motion detector when using the GPU resources. 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) 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 (CPU, key frames) | Service detectors with decoding by key frames when using the СPU resources:
The platform is calculated for one service detector (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 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 detectors (VMDA) based on object tracker when using the СPU resources. The results are given for the object tracker with one active 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 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 using the information in GPU performance for Axxon One detection tools | |
| 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 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 sub-detector | |
| Neurotracker (GPU, 6 FPS) | Scene analytics 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. 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 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:
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 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:
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 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 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 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 Fire&Smoke tab
Name | Description |
|---|---|
Fire detector (CPU, 0.1 FPS) Smoke detector (CPU, 0.1 FPS) | Fire and smoke detectors based on neural network using CPU 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 |
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 |
|---|---|
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. 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 number of specific pose detectors created in the configuration for the Human pose detector parent object does not affect the calculation results (except for the Close-standing people 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. 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 number of specific human pose detectors created in the configuration for the Human pose detector parent object does not affect the calculation results (except for the Close-standing people detector). 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 detector (CPU, 1 FPS) | Personal protection equipment (PPE) detectors based on neural network using CPU 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 results are given for a detector with five classification networks operating simultaneously when 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 detector identifies the presence of the necessary equipment |
| Equipment detector (GPU, 1 FPS) | Personal protection equipment (PPE) detectors 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 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 results are given for a detector with five classification networks operating simultaneously when 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 detector identifies 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. |
| 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. |