| Introduction |
|
GV-LPR is a digital
license plate recognition system that utilizes Neural Network
Technology of artificial intelligence to identify vehicle license
plates. It comprises a video capture card to acquire images,
a software-based engine to recognize characters, a set of I/O
modules to integrate with alarm and sensors, and a database
to store images for event retrieval or analysis. 1, 2, or 4-lane
real-time recording are available. |
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| The
Recognition |
|
GV-LPR activates
recognition process in two ways. One is by software motion detection,
which detects vehicles passing through the camera and illuminator
zone. Second is by I/O activation, which the action triggers
recognition and recording on I/O activation. For example, a
GV-LPR may be connected to a sensor such as magnetic loop detector,
overhead laser scanner, or infrared sensor. When these devices
are triggered, the system starts the recognition and sends the
output signals to trigger devices like gate controller. The
system supports two view modes including Camera Overview and
Picture-in-Picture with ease of monitoring. |
|
| The
Integration |
|
The system provides multiple I/O points
for connection to devices such as gate controller, infrared
detector, loop detection system, or alarms. By exporting the
database that logs the captured license plate numbers, GV-LPR
can be integrated to systems such as GV-series DVR or ticketing
system. |
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| Installations |
|
The GV-LPR software
currently recognizes license plates of these countries: UK,
Italy, Spain, Taiwan and Japan. More are to be implemented.
|
|
| GV-LPR
System Architecture |
|
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| Applications |
|
Parking
lot management |
|
Automatic toll
collection enforcement |
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Traffic enforcement
statistics |
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Border surveillance |
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Stolen vehicle
search |
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| Multiple
I/O Integration |
|
The system can
be used in conjunction with card reader, |
| |
TITO (ticket-in/ticket-out
system), and gate control system |
|
Outputs license
plate numbers through RS232 or by file for various integration |
|
Hot Key Support
can be used to manually activate I/O devices |
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| Installations |
|
High recognition
rate: 99% (at best performance) |
|
High recognition
speed: < 0.3 second |
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Integrates with
GV-series DVR to retrieve video footage by license plate number |
|
Vehicle counter
feature tracks number of vehicles remaining in the parking lot
|
| |
by counting vehicles
entering and exiting the gate |
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Camera overview
brings focuses on either the vehicle or the driver |
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License plate recognition
database are processed and stored in |
| |
Microsoft Access
Database for easy and multiple applications |
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Multiple recognition
technology is applied until a recognition result is reached |
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Alarms will be
triggered when an unauthorized or unrecognizable |
| |
vehicle is spotted |
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Picture-in-Picture
window provides camera overview and |
| |
the last recognized
image |
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Video or AVI file
is used as recognition input source |
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Digital watermark
technology is applied to verify authenticity of video clips |
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Text overlay displays
license plate numbers on recorded video image |
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Activates recognition
by sensor triggers, video motion detection or Hot Key |
|
Recognition result
triggers I/O Relay or user-defined application |
|
Recognizes and
displays license plate image registered in database |
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Retrievable vehicle
entry and exit records by license plate number |
|
JPEG format support:
Allows approximately 10 million JPEG images |
| |
to be saved in
a 80GB HDD |
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Auto recycle for
image storage and database backup |
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| Specifications |
| Model |
GV-LPR
1 |
GV-LPR
2
|
GV-LPR
4
|
Number
of Lanes |
1
Port |
2
Ports |
4
Ports |
Total
FPS |
30fps |
60fps |
120fps |
FPS
/ Camera |
30fps |
30fps |
30fps |
Image
Format |
JPEG |
JPEG
|
JPEG |
CPU |
Pentium
4 1.8GHz |
Pentium
4 2.4GHz |
Pentium
4 3.0GHz |
OS |
Window
2000/XP |
Window
2000/XP |
Window
2000/XP |
HDD |
40GB |
40GB |
80GB |
RAM |
256MB |
256MB |
512MB |
|
|
| Camera
Requirements |
| Illumination
|
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| Minimum
Resolution |
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| Auto
Electronic Shutter |
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| Auto
White Balance Compensation |
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| Backlight
Compensation |
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