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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

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

Service detectors with decoding by key frames when using the СPU resources:

• Quality degradation.
• Blurred Image Detection.
• Compression Artifacts Detection.
• Image Noise Detection.
• Scene change.

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

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

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

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

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

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

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:

• 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 sub-detector

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:

• 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 sub-detector

License plate recognition VT detector when using the СPU resources

Detector recognizes makes, models, type, color and running lights of RR vehicles when using СPU resources

Face detector when using СPU resources

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

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

Object trajectories detection tool.

Motion Detection

NOT a smart video detection tool (Motion detection).

Motion Detection (decode key frames)

NOT a smart video detection tool (Motion detection) with key frames decoding enabled.

License plate recognition detection tool

Face search detection tool

500 MB of video memory per detection type is required regardless of the number of channels. For example, for any number of smoke detection channels, 500 MB is required, and if the server has any number of both smoke and fire detector channels at the same time, a video card with at least 1 GB of memory should be in use.

Several video cards can be in use in one system.

If the Time between processed frames in seconds parameter is set to the default value (10 seconds), any NVIDIA graphics card compatible with the detection tool will be suitable (see the requirements in the Axxon Next User Guide).

The number of specific pose detection tools created under the Pose detection object does not affect the calculation results (except Close-standing people detection which contributes to the overall load).

The platform is calculated for Delay between two measurements value of 333 ms (i.e. 3 fps which is different from the default value).

Object trajectories detection tool (VMDA) based on neural network and using the GPU resources

For each track, one image per second is sent to neural network for classification.

• The NVIDIA GeForce GT 730 video card is capable of processing about 70* classifications** per second.
• The NVIDIA GeForce GTX 1070 video card is capable of processing about 220*** classifications per second.
• The NVIDIA Tesla P40 video card is capable of processing about 122**** classifications per second.
  

• The Intel Neural Compute Stick 1 (movidius I) is capable of processing about 58***** classifications per second.

• The Intel Neural Compute Stick 2 (movidius II) is capable of processing about 200***** classifications per second.
  

Several video cards can be in use in one system.

For example, if you need to track 9 persons per second on 10 cameras, GeForce GTX 1070 or similar video card is suitable.

Up to two Intel Neural Compute Stick can be in use in one system.

Axxon Next detection tools:

• Loss of quality;
• Blurred Image Detection;
• Compression Artifacts Detection;
• Image Noise Detection;
• Position change.

; the frame rate of the incoming video stream is usually higher

Fire detector (GPU, 0.

1 FPS)

Smoke detector (GPU, 0.1 FPS)