| Description | To train neural networks, |
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you must collect and submit to AxxonSoft |
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videos and images from your actual cameras taken in the same resolution and |
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in the same conditions as in your future application. For example, if you need your neural network |
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to work outdoors, videos must contain all |
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weather conditions (sun, rain, snow, fog, |
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and so on) at different times of day ( |
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evening, and night). If the collection requirements for the data submitted for training the neural network model are met, and if you operate the neural network in conditions that are as similar as possible to the conditions in which the data was collected, we guarantee the overall accuracy (training in operating conditions) of neural network analytics from 90% to 97% and the percentage of false positives of 5-7%. For standard neural networks (training wasn't performed in operating conditions), we guarantee the overall accuracy of 80-95% and the percentage of false positives of 5-20%. The standard neural networks are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directory. The requirements can be changed or added to at any time |
| General requirements for collected data |
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- data, specific requirements for object images, scene, angle, lighting,
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- detectors that you plan to use (see
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a neural network in different conditions of time of day, lighting, angle, object types, or weather,
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Example. It is necessary to detect a person in the surveillance area at night and during the day. - Data collected correctly:
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- four videos of the surveillance area, each
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- five minutes long are submitted for training;
- the object of interest appears in the frame in each video
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- two videos are recorded in
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- two—in daytime conditions.
- Data collected incorrectly:
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- three videos of the surveillance area, each
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- five minutes long are submitted for training;
- the object of interest appears in the frame in each video
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- one—in daytime conditions
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| Additional requirements for |
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| collected data for each neural analytics tool |
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Tool Filter| filter | At least 1000 frames containing different objects of interest in the given scene conditions, and the same number of frames containing no objects (noise frames) | | Neural |
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Tracker3 to 5 video | videos containing objects of interest in the given scene conditions. The |
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more | greater the number and variability of |
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the | situations in the scene, the better |
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Posture detection tools seconds of second video of a scene with no persons.
At least 100 different persons in the given scene conditions.
Attention! Different conditions mean, among others, different |
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postures an individual tilting limbs patterns, etc.)Personal protective equipment detection toolspositions of body parts, and so on) | Equipment detector | A list of all reference equipment with examples |
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should must be collected from the object and |
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agreed Several video recordings each of videos with personnel in the given scene conditions.
Personnel |
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should posture positions in the collected |
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video recordingsvideos, as well as remove and put on equipment at intervals of 30 seconds.
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Personal protective detection tools detectors are designed for artificial constant lighting, |
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video recordings videos in other lighting conditions |
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are not requiredFire and Smoke detection toolsaren't required | | Equipment detector VL | | Fire detector | At least 1000 frames containing different objects of interest in the given scene conditions, and the same number of frames containing no objects (noise frames) |
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Object presence detection tool| Smoke detector | | Neural classifier | At least 1000 frames containing different objects of interest in the given scene conditions, and the same number of frames containing no objects (noise frames) |
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Food recognition * | Images of at least 80% of the actual menu items should be provided. Each menu item requires 20 to 40 images shot in different conditions. |
If the above requirements for the collection of data transmitted for training the neural network model are met, and if the neural network is operated in the conditions that are as similar as possible to the conditions in which the material for its training was collected, then the overall accuracy** of neural network analytics is guaranteed from 90% to 97% and the percentage of false positives is 5-7%. For general networks***, an overall accuracy of 80-95% and a false positive rate of 5-20% are guaranteed.
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* Will be available in future versions of Axxon One. ** Accuracy is indicated for a neural network model, which was trained under operating conditions. *** A general network is a network that was not trained under operating conditions. |
