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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

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

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

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

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

Scene analytics detection tools based on neurotracker using GPU 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:

• 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 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

Scene analytics detection tools based on neurotracker using CPU 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:

• 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.

The results are given for neurotracker with one active Line Crossing detection sub-tool

Face detection tool when using СPU resources

Face detection tool when using GPU resources. The GPU decoder operation mode was used