Visual Faculty Environent (VFE)
«detail»
VFE - Local Scenery
«detail»
Camera
«interface»
video
«concrete»
ifToStart
Timetable
«detail»
Detecting the student group
«concrete»
checking for
the facial
«interface»
checking
ifStudenGroup
«policy»
Emotion analysis
<<Interface>>
Portrait Buffer
«concrete»
Define the
Picture
+ field1: totalAmount(Pcs)
+ field2: totalAmount(Mbs)
+ checkMemory(): bool
+ clearMemory(): void
+ insertPortrait(): void
«model»
Making a
single portrait
«interface»
Making a total portrait
«policy»
VFE - Managing sceneries
«policy»
CheckActivity
«detail»
Choosing content
Defining subject area
«interface»
Searching for actual
vacancies/last
scientific
achievements/online
courses
+ field: keyword1
+ field: keyword2
+ field: keyword3
«interface»
Determining the activity regime
+ makeARequest(): void
fields are
used to store
the keywords
of subject
area
«concrete»
Vizualization on
screen
«interface»
Send signals to sceneries
«detail»
VFE - Global Scenery
«detail»
CheckActivity
«detail»
Camera
«interface»
Video
«concrete»
Video Flow
«concrete»
Video Cut
Before starting the process of
facial recognition, the program
should cut a piece from the
video flow of our camera. The
length of it should be
about 5 minutes - the most
appropriate length for Intel's
OpenVINO toolkit library.
«interface»
Storing the portraits
«detail»
Facial Recognition
Flow Handler
MemoryBuffer
+ field: VIdeoPart
+ field: VIdeoPart
+ field: totalAmount (Mbs)
+ CheckFPS(): bool
+ CheckRTSP(): bool
+ CheckMemory(): bool
+ ClearMemory(): void
+ insertVideoPart(): void
«concrete»
Sending to database
Video Analysis (OpenVINO)
MemoryBuffer (Screenshots)
+ field: VIdeoPart
+ field: Face
+ field: totalAmount (Pcs)
+ field: totalAmount (Mbs)
+ FacialCheck(): bool
+ CheckMemory(): bool
+ ClearMemory(): void
+ insertScreenshot(): void
«interface»
Facial Screen
«concrete»
ClearMemory
«detail»
Cropping&Normalize
«concrete»
Cropping
«concrete»
Normalize
«interface»
Normalized Image
Execute once per month
on closing dates
«policy»
Emotion Recognition
Image Distortion
«concrete»
Training the neural network
+ field: Image
+ DefineLayer(): void
PortraitBuffer
«concrete»
Producing the portraits
+ field: Face
+ field: totalAmount (Pcs)
+ field: totalAmount (Mbs)
+ field: metrics
+ CheckMemory(): bool
+ ClearMemory(): void
+ insertMetricPortrait(): void
«concrete»
Producing the total portrait
Total portrait produces
once during the
determined time period
(1h as usual)
After this process the
portrait еру buffer
should be cleaned
«model»
Total portrait
«Detail»
Ambient Environment
«interface»
Choosing an ambient scenery
«concrete»
Launching an ambient scenery
Portraits per
month