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The Application of Artificial
Intelligent Technology in the Field of
Intelligent Transportation
Vion Technologies Inc.
www.vion-tech.com
Computer Vision (Image Recognition)
……
Artificial Intelligence Technology
Com
mer
ce
Fina
nce
Tran
spor
tatio
n
Logi
stics
Enviro
nment
al
Protec
tion
Home
Furnis
hing
Medic
al
Treat
ment
Natural Language Processing, Speech Recognition
Robotics
Module Recognition, Data Mining
Statistical Learning
— 3 —
Using AI technology to form urban decision-making
mechanisms driven by data, and control and coordinate public
resources according to real-time data; better saving investment
costs of human resources and capital; aiding in the decision-
making on future urban planning based on the big data of the
city in previous years; guiding the smart transformation of
newly-built cities; improving satisfaction of city-dwellers,
enhancing the service quality of the government and achieving
the synergetic development of people, vehicles and roads.
Artificial Intelligence – the Brain of Smart Cities
*The “Data Brain of the City” implemented in Hangzhou, China
utilizes AI technology + 50,000cameras = to reduce the deployment of
traffic police by150,000
People
Vehicles
Roads
— 4 —
Blue, Car, Honda
Abnormal running of
persons below the bridge
Congestion below
the bridge all year
round
High passenger flow at
the entrance to subway
High vehicle flow
exiting from the city
Additional buses
needed for passengers
held up at the bus stop
Congestion caused by
abnormal illegal parking
Intrusion of stranger at the north
gate of the residential compound
Access control needed for increased
pedestrian flow at scenic area
Vehicle going in the
wrong direction
Blacklisted vehicle
Intrusion into
restricted area
Hit-and-run
vehicle
Congestion at this
section
Vehicle running
the red light
ATM violence
incident
AI Contributes to the Upgrading of Smart City Management
High pedestrian and vehicle
flows near the airport
Congestion at the
entrance to high-
speed rail station
Road section G7,
accident, congestion
Crowd gathering at
AAA Square
Intelligent Transportation System (ITS)
Construction Philosophy of ITS
ITS Planning
Technology, Products
System Construction
Operation/Management
/Environment
Highway/Vehicles
People
Safe
EfficientSmooth
Environment-
Friendly
Construction of
roads
Collection of traffic
information
Building the operation and management
system
Providing service support to road users
Construction of the social infrastructure system
ITS
ITS and Artificial Intelligence Technology
Transportation
Management and Planning
Electronic Toll Collection
Travelers' Information
Vehicle Safety and Assisted
Driving
Emergency Rescue and
Security
Freight Management
Comprehensive
Transportation
Automatic Highway
Knowledge Representation
Method
Searching and Reasoning
Techniques
Computational Intelligence
The Expert System
Machine Learning
Intelligent Agent
Intelligent Control
Natural Language
Understanding
ITS Artificial Intelligence
Technology
— 7 —
The number of
motor vehicles is
growing rapidly
Traffic violations
are severely
hazardous
Traffic congestion
is gradually
intensifying
By the end of 2015, the possession of motor vehicles
nationwide had reached 0.279 billion with that of cars
reaching 0.172 billion. The annual growth and the
number of newly registered vehicles have both
reached a record high.
By the end of 2015, the number of motor vehicle
traffic violations had surpassed 0.2 billion, with
running the red lights, illegal parking, crossing
forbidden lines and speeding on the top of the list,
indicating relatively poor safety awareness of drivers.
The 2015 Report on Traffic in China’s Major Cities
indicates that traffic congestion in China has been
further worsening.
Background Introduction
$
Electronic Police
Cloud Electronic Police
Solutions Provided for Intelligent Transportation Industry
To relieve the current situation of traffic congestion, improve the urban traffic environment, and meanwhile
to regulate traffic violations and cut down on traffic accidents, Vion has independently developed a series of
products for intelligent transportation, and mainly provides the following five solutions:
Snapping System
Concerning Illegal Use of
Specialized Lanes
Inspection
Control System
Comprehensive
Supervision on
Violations by Dome
Cameras
Travelers'
Information
— 9 —
Core Equipments
• Based on the Ambarella high-
performance platform, dual
1GHz
Cortex-A9 processors
• large-area CCD with high
photosensitivity
• Support 2 interfaces for SD
Card
• Support 3G/4G/WIFI
• Sensor-rich (9
spindles/Temperature sensor)
Omni-Directional
Sensing Camera
• Based on the NIVIDIA
platform, 2.3GHz
processor, 196 core GPU
processor
• Support USB 3.0 interface
• 4 *3.5” SATA hard disk)
• Support LED Monitor
Intelligent Control
Terminal
• Based on the NIVIDIA platform,
40 CPU chips
• Loaded with the world’s
leading algorithms of face
recognition
• Computing power reaching 12
TFLOPS
• Able to analyze 80*1080P video
simultaneously
• Support mixed use of multiple
detection algorithms
• Support networking of multiple
equipments
Fanxin Smart Analysis
Cluster Server
• Based on NVIDIA platform,
support GPU parallel
computing
• Deep learning algorithm
based on Neural Network
• network video signal access
• Support 2.5 inch hard disk
and EMMC motherboard
storage
• Support USB3.0, dual
Gigabit Ethernet port
Smart Analysis
Box
— 10 —
Smart Camera
6.8 million 6.08 million
2008
3392
2208
2752
8.3 million
2160
3840
Independently Developed High-resolution Smart Camera
4/3 inch CCD
 6.08 million pixels (2752*2208)
 6.80 million pixels (3392*2008)
 8.30 million pixels(3840*2160)
— 11 —
Model recognition (7 kinds)
Recognition of body color (16 kinds)
Logo recognition (12 kinds)
Recognition of license plate color
(5 kinds)
Intelligent Control Terminal based on CPU with high computational capacity,
Double recognition of front-end pictures
Face capturing and cutout
Detection of no seat belt while driving
Detection of using cellphones
while driving
Detection of yielding to
pedestrians
GPU
nVidia Tegra K1
Intelligent Analysis Terminal Based on Deep Learning
Deep learning algorithms based on
Convolutional Neural Network (CNN)
— 12 —
Strengthening traffic security: snapshot of vehicles
— 13 —
Strengthening traffic security: snapshot of running red lights
— 14 —
Strengthening traffic security: yielding to pedestrians
— 15 —
Strengthening traffic security: illegal parking
— 16 —
Collection of traffic parameters:
• Information of traffic flow
• Queuing length, waiting time
• Average travel speed
• Statistical analysis of vehicle model
Improving traffic efficiency: detection of traffic flow
— 17 —
Improving traffic efficiency: detection of traffic flow
— 18 —
Note: all dates above are during daytime . For night data, please refer to specific algorithms introduction in the
document
With deep learning algorithms, the high accuracy
rate maintains a continuously growing trend
Accuracy rate: Recognition of
vehicle license plate
Effective rate : Recognition
of running red
lights
Effective rate :
Recognition of crossing
forbidden lines
Effective rate : Recognition of
converse driving
Effective rate : Recognition of
occupying
non-motor vehicle lanes
Effective rate : Recognition of
occupying
bus-exclusive lanes
Effective rate : Recognition of
forbidden trucks
on certain roads
Effective rate :
Recognition of
not yielding to
pedestrians
95% 95%
98% 98%
98%
95% 80%
95%
High accuracy
— 19 —
Cloud Electronic Police
StarNet
GPU Cluster Server of Video Analysis
— 20 —
Various
camera
brands
HikVision/ahua/Uniview/Skyworth/
Samsung/Sony/Bosch/Axis
Various
resolutio
ns
D1/720P/1080P/2048*1
536/2752*2208/3840*2
160
Various
video
formats
h264/mpeg4
Various
protocols
onvif protocol/28181
protocol/
rtsp protocol
High compatibility
2U Double-Path E5 High-performance ServerStarNet High-Density Video Analysis Server
Parallel analysis of 80-
channel 1080P video
Quad core CPU+192 GPU CUDA cores
8-channel parallel analysis
Six core CPU
1 10
>
s e t s e t s
40 GPU chips 2 CPU chips
12TFLOPS /
High performance
Number of processors
Number of single-processors
The number of Channels for parallel analysis
Computing capacity
— 22 —
Multiple algorithms for recognition
Converse
driving
Recognitio
n of
vehicles
and their
behaviors
Recognitio
n of
persons
and their
behaviors
Crossing
forbidde
n lines
Illegal
parking
Straying from
regulated lines
Occupying
lanes for
exclusive use
Record of
vehicle images
Congestion
detection
Statistical analysis
of traffic flow
Forbidden
trucks
Vehicle
model
Color of
vehicle body
Vehicle
brand
Boundary
guard
Detection of
crossing forbidden
lines
Running Fighting Recognition of
masked face
Face
recognition
Height
Detection of
stalls
Detection of
crowd density
Assistant Inspection
control system
Age Detection of illegal
motorbikes
— 23 —
Large-scale application
Release
results
Analysis of static images
Big Internet database Exclusive database
Real-time analysis
Intelligent traffic
management
Information log library
Data
Collection
Data
Analysis
Data
application
Multi-user retrieval Multi-channel monitoring
Incident
analysis
Trajectory analysis
Common camera
Data
Management
Data storage Data integration Data connection
Data aggregation Information codingVideo structuring
Search by image
Attribute retrieval
Comprehensive analysis
Delay analysis
Smart traffic camera Mobile camera
— 24 —
Smart Analysis Box
• Based on the NVIDIA platform, supports GPU
parallel computing
• Deep learning algorithm based on Neural
Network
• network video signal access
• Supports 2.5 inch hard disks and EMMC
motherboard storage
• Supports USB3.0, dual Gigabit Ethernet port
— 25 —
Transportation Hub – Airport
Passenger Flow Analysis at the
Departure Hall of Zhengzhou Airport
Monitoring of Illegal Parking at Kunming
Changshui International Airport
Passenger Flow Analysis; Key Area Analysis; Pedestrian Flow Prediction;
Information of Neighboring Vehicle Flow
— 26 —
Public Transport – Buses
At the Bus Stop Inside Buses
Passenger Volume Statistics, Analysis and Prediction; Evaluation on the
Rationality of Routes; Early Warning of Ticket Fares; Smart Dispatching of Buses
— 27 —
Transportation Hub – Subway
Analysis of Passenger Flow outside the Subway Station Analysis of Passenger Flow in Passageways of the
Subway
Passenger Flow; Direction, Trend and Distribution of Passenger Flow; Waiting
Time; Degree of Crowding; Surrounding/Passageway Situation
— 28 —
Transportation Hub – Subway
Analysis of Passenger Flow at the Platform of the Subway
— 29 —
Transportation Hub – Subway
Analysis of Passenger Flow inside the Carriages of the Subway
VISION WITHOUT LIMITS!
About VION
 Vion Technologies Inc., established by internationally renowned experts in fields of computer vision and artificial intelligent, is
equipped with a strong research & development team comprised of master and doctoral professional talents. It is one of the most
professional R&D team of intelligent products in computer vision and artificial intelligence, with a total of 200 staff and over 50%
being research fellows. All the core algorithms and hardware are developed from the bottom and we hold fully independent
intellectual property.
 Vion Technologies Inc. is an “NEEQ" corporation, namely a small and medium-sized listed company (stock code: 838382), with
the headquarters located in Beijing Shangdi Science Park, Beijing and 26 representative offices (by mid-2016) in various major
cities in China.
 In the past ten years, we have progressed from core algorithms and hardware & software integrated products to complete
solutions. We have formed a smart traffic product line covering electronic police, inspection control, and dome cameras for
comprehensive violation monitoring, vehicle-mounted camera for capturing illegal use of specialized lanes, and cloud electronic
police, as well as a product line for statistically analyzing passenger flow in commercial real estate, chain stores, scenic spots,
exhibition halls, buses, subway and railway. Besides, we have also developed a product line for intelligent security and protection
applicable to smart cities, financial banks, public security, as well as the power and oil industries.
Thanks for Viewing
Vion Technologies Inc.

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Practices of AI guided traffic analysis

  • 1. The Application of Artificial Intelligent Technology in the Field of Intelligent Transportation Vion Technologies Inc. www.vion-tech.com
  • 2. Computer Vision (Image Recognition) …… Artificial Intelligence Technology Com mer ce Fina nce Tran spor tatio n Logi stics Enviro nment al Protec tion Home Furnis hing Medic al Treat ment Natural Language Processing, Speech Recognition Robotics Module Recognition, Data Mining Statistical Learning
  • 3. — 3 — Using AI technology to form urban decision-making mechanisms driven by data, and control and coordinate public resources according to real-time data; better saving investment costs of human resources and capital; aiding in the decision- making on future urban planning based on the big data of the city in previous years; guiding the smart transformation of newly-built cities; improving satisfaction of city-dwellers, enhancing the service quality of the government and achieving the synergetic development of people, vehicles and roads. Artificial Intelligence – the Brain of Smart Cities *The “Data Brain of the City” implemented in Hangzhou, China utilizes AI technology + 50,000cameras = to reduce the deployment of traffic police by150,000 People Vehicles Roads
  • 4. — 4 — Blue, Car, Honda Abnormal running of persons below the bridge Congestion below the bridge all year round High passenger flow at the entrance to subway High vehicle flow exiting from the city Additional buses needed for passengers held up at the bus stop Congestion caused by abnormal illegal parking Intrusion of stranger at the north gate of the residential compound Access control needed for increased pedestrian flow at scenic area Vehicle going in the wrong direction Blacklisted vehicle Intrusion into restricted area Hit-and-run vehicle Congestion at this section Vehicle running the red light ATM violence incident AI Contributes to the Upgrading of Smart City Management High pedestrian and vehicle flows near the airport Congestion at the entrance to high- speed rail station Road section G7, accident, congestion Crowd gathering at AAA Square
  • 5. Intelligent Transportation System (ITS) Construction Philosophy of ITS ITS Planning Technology, Products System Construction Operation/Management /Environment Highway/Vehicles People Safe EfficientSmooth Environment- Friendly Construction of roads Collection of traffic information Building the operation and management system Providing service support to road users Construction of the social infrastructure system ITS
  • 6. ITS and Artificial Intelligence Technology Transportation Management and Planning Electronic Toll Collection Travelers' Information Vehicle Safety and Assisted Driving Emergency Rescue and Security Freight Management Comprehensive Transportation Automatic Highway Knowledge Representation Method Searching and Reasoning Techniques Computational Intelligence The Expert System Machine Learning Intelligent Agent Intelligent Control Natural Language Understanding ITS Artificial Intelligence Technology
  • 7. — 7 — The number of motor vehicles is growing rapidly Traffic violations are severely hazardous Traffic congestion is gradually intensifying By the end of 2015, the possession of motor vehicles nationwide had reached 0.279 billion with that of cars reaching 0.172 billion. The annual growth and the number of newly registered vehicles have both reached a record high. By the end of 2015, the number of motor vehicle traffic violations had surpassed 0.2 billion, with running the red lights, illegal parking, crossing forbidden lines and speeding on the top of the list, indicating relatively poor safety awareness of drivers. The 2015 Report on Traffic in China’s Major Cities indicates that traffic congestion in China has been further worsening. Background Introduction
  • 8. $ Electronic Police Cloud Electronic Police Solutions Provided for Intelligent Transportation Industry To relieve the current situation of traffic congestion, improve the urban traffic environment, and meanwhile to regulate traffic violations and cut down on traffic accidents, Vion has independently developed a series of products for intelligent transportation, and mainly provides the following five solutions: Snapping System Concerning Illegal Use of Specialized Lanes Inspection Control System Comprehensive Supervision on Violations by Dome Cameras Travelers' Information
  • 9. — 9 — Core Equipments • Based on the Ambarella high- performance platform, dual 1GHz Cortex-A9 processors • large-area CCD with high photosensitivity • Support 2 interfaces for SD Card • Support 3G/4G/WIFI • Sensor-rich (9 spindles/Temperature sensor) Omni-Directional Sensing Camera • Based on the NIVIDIA platform, 2.3GHz processor, 196 core GPU processor • Support USB 3.0 interface • 4 *3.5” SATA hard disk) • Support LED Monitor Intelligent Control Terminal • Based on the NIVIDIA platform, 40 CPU chips • Loaded with the world’s leading algorithms of face recognition • Computing power reaching 12 TFLOPS • Able to analyze 80*1080P video simultaneously • Support mixed use of multiple detection algorithms • Support networking of multiple equipments Fanxin Smart Analysis Cluster Server • Based on NVIDIA platform, support GPU parallel computing • Deep learning algorithm based on Neural Network • network video signal access • Support 2.5 inch hard disk and EMMC motherboard storage • Support USB3.0, dual Gigabit Ethernet port Smart Analysis Box
  • 10. — 10 — Smart Camera 6.8 million 6.08 million 2008 3392 2208 2752 8.3 million 2160 3840 Independently Developed High-resolution Smart Camera 4/3 inch CCD  6.08 million pixels (2752*2208)  6.80 million pixels (3392*2008)  8.30 million pixels(3840*2160)
  • 11. — 11 — Model recognition (7 kinds) Recognition of body color (16 kinds) Logo recognition (12 kinds) Recognition of license plate color (5 kinds) Intelligent Control Terminal based on CPU with high computational capacity, Double recognition of front-end pictures Face capturing and cutout Detection of no seat belt while driving Detection of using cellphones while driving Detection of yielding to pedestrians GPU nVidia Tegra K1 Intelligent Analysis Terminal Based on Deep Learning Deep learning algorithms based on Convolutional Neural Network (CNN)
  • 12. — 12 — Strengthening traffic security: snapshot of vehicles
  • 13. — 13 — Strengthening traffic security: snapshot of running red lights
  • 14. — 14 — Strengthening traffic security: yielding to pedestrians
  • 15. — 15 — Strengthening traffic security: illegal parking
  • 16. — 16 — Collection of traffic parameters: • Information of traffic flow • Queuing length, waiting time • Average travel speed • Statistical analysis of vehicle model Improving traffic efficiency: detection of traffic flow
  • 17. — 17 — Improving traffic efficiency: detection of traffic flow
  • 18. — 18 — Note: all dates above are during daytime . For night data, please refer to specific algorithms introduction in the document With deep learning algorithms, the high accuracy rate maintains a continuously growing trend Accuracy rate: Recognition of vehicle license plate Effective rate : Recognition of running red lights Effective rate : Recognition of crossing forbidden lines Effective rate : Recognition of converse driving Effective rate : Recognition of occupying non-motor vehicle lanes Effective rate : Recognition of occupying bus-exclusive lanes Effective rate : Recognition of forbidden trucks on certain roads Effective rate : Recognition of not yielding to pedestrians 95% 95% 98% 98% 98% 95% 80% 95% High accuracy
  • 19. — 19 — Cloud Electronic Police StarNet GPU Cluster Server of Video Analysis
  • 21. 2U Double-Path E5 High-performance ServerStarNet High-Density Video Analysis Server Parallel analysis of 80- channel 1080P video Quad core CPU+192 GPU CUDA cores 8-channel parallel analysis Six core CPU 1 10 > s e t s e t s 40 GPU chips 2 CPU chips 12TFLOPS / High performance Number of processors Number of single-processors The number of Channels for parallel analysis Computing capacity
  • 22. — 22 — Multiple algorithms for recognition Converse driving Recognitio n of vehicles and their behaviors Recognitio n of persons and their behaviors Crossing forbidde n lines Illegal parking Straying from regulated lines Occupying lanes for exclusive use Record of vehicle images Congestion detection Statistical analysis of traffic flow Forbidden trucks Vehicle model Color of vehicle body Vehicle brand Boundary guard Detection of crossing forbidden lines Running Fighting Recognition of masked face Face recognition Height Detection of stalls Detection of crowd density Assistant Inspection control system Age Detection of illegal motorbikes
  • 23. — 23 — Large-scale application Release results Analysis of static images Big Internet database Exclusive database Real-time analysis Intelligent traffic management Information log library Data Collection Data Analysis Data application Multi-user retrieval Multi-channel monitoring Incident analysis Trajectory analysis Common camera Data Management Data storage Data integration Data connection Data aggregation Information codingVideo structuring Search by image Attribute retrieval Comprehensive analysis Delay analysis Smart traffic camera Mobile camera
  • 24. — 24 — Smart Analysis Box • Based on the NVIDIA platform, supports GPU parallel computing • Deep learning algorithm based on Neural Network • network video signal access • Supports 2.5 inch hard disks and EMMC motherboard storage • Supports USB3.0, dual Gigabit Ethernet port
  • 25. — 25 — Transportation Hub – Airport Passenger Flow Analysis at the Departure Hall of Zhengzhou Airport Monitoring of Illegal Parking at Kunming Changshui International Airport Passenger Flow Analysis; Key Area Analysis; Pedestrian Flow Prediction; Information of Neighboring Vehicle Flow
  • 26. — 26 — Public Transport – Buses At the Bus Stop Inside Buses Passenger Volume Statistics, Analysis and Prediction; Evaluation on the Rationality of Routes; Early Warning of Ticket Fares; Smart Dispatching of Buses
  • 27. — 27 — Transportation Hub – Subway Analysis of Passenger Flow outside the Subway Station Analysis of Passenger Flow in Passageways of the Subway Passenger Flow; Direction, Trend and Distribution of Passenger Flow; Waiting Time; Degree of Crowding; Surrounding/Passageway Situation
  • 28. — 28 — Transportation Hub – Subway Analysis of Passenger Flow at the Platform of the Subway
  • 29. — 29 — Transportation Hub – Subway Analysis of Passenger Flow inside the Carriages of the Subway
  • 30. VISION WITHOUT LIMITS! About VION  Vion Technologies Inc., established by internationally renowned experts in fields of computer vision and artificial intelligent, is equipped with a strong research & development team comprised of master and doctoral professional talents. It is one of the most professional R&D team of intelligent products in computer vision and artificial intelligence, with a total of 200 staff and over 50% being research fellows. All the core algorithms and hardware are developed from the bottom and we hold fully independent intellectual property.  Vion Technologies Inc. is an “NEEQ" corporation, namely a small and medium-sized listed company (stock code: 838382), with the headquarters located in Beijing Shangdi Science Park, Beijing and 26 representative offices (by mid-2016) in various major cities in China.  In the past ten years, we have progressed from core algorithms and hardware & software integrated products to complete solutions. We have formed a smart traffic product line covering electronic police, inspection control, and dome cameras for comprehensive violation monitoring, vehicle-mounted camera for capturing illegal use of specialized lanes, and cloud electronic police, as well as a product line for statistically analyzing passenger flow in commercial real estate, chain stores, scenic spots, exhibition halls, buses, subway and railway. Besides, we have also developed a product line for intelligent security and protection applicable to smart cities, financial banks, public security, as well as the power and oil industries.
  • 31. Thanks for Viewing Vion Technologies Inc.

Editor's Notes

  1. 路口拥堵检测过程: 在绿灯时出现拥堵现象,摄像机联动大屏提示路口拥堵不要通行 但车辆仍旧继续行驶且在信号灯变为红灯时仍旧没有通过该路口则判定为路口滞留 路口滞留违法抓拍三张全景图片,全景 1、全景 2 为信号灯为绿灯,全景 3 信号灯为红灯