Page History
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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 one active Motion In Area detection sub-tool | |
| AI tracker with a neural filter (CPU) | 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 | |
| AI tracker with a neural filter (GPU) | 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 | |
| Neurotracker (CPU, 6 FPS) | 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 | |
| Neurotracker (GPU, 6 FPS) | 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 | |
| Neurotracker (CPU, 6 FPS)—Person and Vehicle | 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:
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 | |
| Neurotracker (GPU, 6 FPS)—Person and Vehicle | 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:
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 | |
| Neural counter (GPU, 1FPS) | Scene analytics detection tool 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 detection sub-tool. 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. You can select the type of recognition object for the detection tool: Person, Person (top-down view), Vehicle. | |
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) | Detection tool recognizes makes, models, type, color and running lights of RR vehicles when using СPU resources | |
| Vehicle make and model recognition RR (GPU) | Detection tool 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) detection tool when using СPU resources | |
| License plate, make and model recognition RR (GPU) | License plate recognition RR with enabled Make and model recognition (MMR) detection tool when using GPU resources | |
| License plate recognition IV (CPU) | License plate recognition IV detection tool when using СPU resources | |
| License plate recognition IV (GPU) | License plate recognition IV detection tool when using GPU resources | |
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Name | Description |
|---|---|
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 |
Fire detection tool (GPU, 0.1 FPS) Smoke detection tool (GPU, 0.1 FPS) | Fire and smoke detection tools based on neural network using GPU 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 |
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