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The following Intellect following Axxon PSIM x64 and vertical solutions detection tools detectors grouped by tabs are available for selection.
Base
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detectors
Name | Description | |
|---|---|---|
| Motion | ||
| detection ( |
| CPU) | Base | |
| detector (activated when the camera is armed) for decoding by all frames when using the CPU resources. |
| The results are given with the registry key DetectionFps = 25. Changing the value of the DetectionFps key does not significantly affect the load | ||
Motion detection (CPU, key frames) | Base detector (activated when the camera is armed) for decoding by key frames | |
when using the CPU resources. Changing the frame rate in the settings of the detector (the Frames processed per second parameter) does not significantly affect the load | ||
| Service detection (CPU, key frames) | Service | |
detectors for decoding by key frames when using the CPU resources:
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The platform is calculated for one service |
detector (any of the listed). The |
results are given for decoding by key frames |
with GOP=25 (every 25th frame is the key frame). The detector is applicable only for H.264 and H.265 codecs |
Tracker
Name | Description | |
|---|---|---|
| Tracker VMDA (CPU) | VMDA | |
detectors based on object trajectory tracker |
VMDA detection tools based on object tracker with use of a neural filter and GPU resources. In this case, the CPU decoder operation mode was used.
The models and the number of GPUs are selected separately using the information on the GPU performance for Intellect detection tools page.
VMDA detection tools based on neural tracker as part of Intellect Detector Pack with use of CPU resources.
The frame rate specified during the Neurotracker module 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*.VMDA detection tools based on neural tracker as part of Intellect Detector Pack with use of VPU resources. In this case, the CPU decoder operation mode was used.
when using the CPU resources | ||
| Neurotracker (CPU, 6 FPS) | The scene analytics detectors based on the Neurotracker when using the CPU resources and resource-intensive neural networks for detection of 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. In brackets is the frame rate that you can specify when configuring the Neurotracker object (the Number of frames processed per second parameter). This is the number of frames per second | |
processed by the module; the frame rate of the incoming video stream is usually higher. The |
results are given for |
the Neurotracker with one working Motion In Area sub-detector |
LPR&Traffic
Name | Description | |
|---|---|---|
| License plate recognition VT (CPU) | License plate recognition module VIT as part of Auto | |
| Axxon PSIM when using the CPU resources | ||
| License plate recognition IV (CPU) | License plate recognition module LPR IntelliVision as part of Auto | |
| Axxon PSIM when using the CPU resources | |
| Vehicle detection | |
| IV (CPU) | Subsystem of traffic flow data collection based on IntelliVision vehicle detection as part of | |
Auto PSIM when using the CPU resources | ||
| License plate recognition RR (CPU) | License plate recognition module RR as part of Auto Axxon PSIM software package when using the CPU resources | |
Face
Name | Description | |
|---|---|---|
| Face recognition VA (CPU) | Face | |
detector based on the |
VA recognition |
module as part |
of Face PSIM when using the CPU resources. When calculating the platform using this |
detector, only the resources for face detection and their vectorization are taken into account. The load from comparing faces with the reference database is not taken into account, because usually a separate server is provided to perform this function |
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Railway
Name | Description | |
|---|---|---|
| Wagon number recognition ITB, passenger (CPU) | Recognizer of railway passenger carriage numbers based on the IntLab module as part | |
of Auto PSIM when using the CPU resources.
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| Wagon number recognition ITB, freight (CPU) | Recognizer of railway freight carriage numbers based on the IntLab module as part | |
of Auto PSIM when using the CPU resources.
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Behavior analytics
Name | Description | |
|---|---|---|
People counter | ||
(CPU) | People counter | |
detector that is a part of |
the Axxon PSIM Detector Pack when using the CPU resources. Optimal parameters for this |
detector are a resolution of 800x600 / 640x360 / 640x480 / 320x240 and FPS in the range from 24 to 30. If |
specified parameters of the video stream do not meet these conditions, then when you select this |
detector, the resolution is set to 320x240 and FPS=24 |
| Heat | |
| map (CPU) | Heat map | |
| detector (detection of "cold/hot" areas of a store) that is a part of |
| the Axxon PSIM Detector Pack when using the CPU resources. The results are given when frame rate in the detector settings (the Frames processed per second parameter) is 25 | |
| Queue length | |
| (CPU) | Queue length | |
| detector that is a part of |
| the Axxon PSIM Detector Pack when using the CPU resources. The results are given when frame rate in the settings of the detector (the Frames processed per second parameter) is 25 |
Fire&Smoke
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Name | Description | |
|---|---|---|
Fire | ||
detector (CPU, 0. |
1 FPS) Smoke |
detector (CPU, 0. |
1 FPS) | Fire and smoke | |
detectors based on neural |
network as part of |
the Axxon PSIM Detector Pack when using the CPU resources. The frame rate specified during the |
detector configuration ( |
the Number of frames for analysis and output 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 |
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| title | Note |
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