Explications sur comment il est possible d'utiliser la puissance d'Hadoop pour analyser les vidéos des caméras présentent sur les réseaux routiers avec pour objectif d'identifier l'état du trafic, le type de véhicule en déplacement et même l'usurpation de plaques d'immatriculation.

1. Big Data and Intel® Intelligent
Systems Solution for Intelligent
Transportation
Xiao Dong Wang, Manager, Big Data Solution Team, Intel
Robin Wang, Platform Solution Architect, Intel
Albert Hu, Solution Architect, Intel
EMBS001

2. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
The PDF for this Session presentation is available from our
Technical Session Catalog at the end of the day at:
intel.com/go/idfsessionsBJ
URL is on top of Session Agenda Pages in Pocket Guide
2

3. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
3

4. China Environment
“Sensing China” (IoT) strategy
-- China Premier Wen Jiabao, ‘09
Government Objective
• Government 12-5Y Plan/Social Harmony
• Determine to lead in Internet of Things (IoT)
‒ Setting international standards for new
technology
‒ $0.8Bn government funds
Mega Trend Challenges
& Government approach “The biggest development potential
lies in the process of urbanization.”
• Urbanization
-- China New Premier Li Keqiang, ‘12
‒ 690Mn now to 900Mn by ‘25
• IoT/Smart City is one way to solve the challenges
‒ IoT market size $80~120Bn by ‘15
‒ 90+ smart cities plans underway
‒ Gaps: Core technology, standards, immature ecosystem, deployment
model
4

5. PRC Transportation Infrastructure
Landscape
1 2
2011 Investment (Bn RMB)
1600
1400
Highway
1200
4,500,000km
1000 Total Number of
Vehicles will exceed
800 200 Million by 2020
600
400
Railway
200
Waterway 120,000km
126,000km Urban Public
0 394,000km
-200
-2% 0% 2% 4% 6% 8% 10%
2011-2015 CAGR (in terms of length)
Infrastructure build-out trending to be stabilized. Key challenges due to
large scale of the infrastructure network, growing number of
vehicles, yet still higher traveler, vehicle/infrastructure density:
Safety, Infrastructure/Traveler’s efficiency, Environment.
1 Source: China Ministry of Transportation’s 12th 5 year plan 2 Source: ISH* Research Report
5

6. Big Data Source From Transportation
Worldwide Enterprise and IP Storage used 0.3PB ~ 6.7PB/Day Video Data
for Video Surveillance by End User (2016) generated for Smart City
Environment
Banking & Finance
2016
Casinos & Gaming
2% 3%
City Surveillance
13% Commercial
Education
37% 7%
3%
Government
7% Industrial, Manufacturing, &
Utilities
Retail
How to effectively
21% Transport
3%
collect, aggregate,
4% Other
manage and analyze data
Source: IMS Enterprise and IP Storage used for Video Surveillance – World-2012
to help Intelligent
Transportation System
(ITS) application?
6

7. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
7

8. Goals of Intelligent Transportation System
• Traffic Management
– Enforcing traffic regulations
– Transportation planning support
– Adaptive traffic control
– Case investigation for police
• Traveler information system
– Real-time road condition
 Speed & congestion
 Historical camera images & statistics
– Travel time information
 Available to various terminals
 Proactive travel plan
• Commercial vehicle systems
– Commercial vehicle management,
tracking, administration
• Public security
– Video surveillance (remote video
streaming & video searching)
8

9. Intelligent Transportation System (ITS)
End-to-End Solution
NVR/DVR/
Hybrid NVR
Decoder
Collect, store, Data Center
Edge transform, analyze Morphology
Edge Video
Server • Embedded
Analytic and mine
• Cloud service
• Proprietary
• High-performance
Data Center Solutions
Distributed Filesystem – HDFS*
Terminal Device
Abundant data visualization Distributed data analysis – Hadoop*
Data analysis and cache Distributed real-time database – HBase*
9

10. Intelligent Transportation System (ITS)
Cross Region Deployment
10

11. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
11

12. Scale Up or Scale Out
Intelligent
Transportation
System (ITS)
Data Burst
Relational Database Distributional Database
shift left shift right
12

13. Intelligent Transportation System (ITS)
Software Architecture
MapReduce Online/Interactive
Data Mining
Offline analysis Applications
HBase*
Distributed
Database
for texts & images
Sqoop*
Data RDB
Integration
Aggregated results
Legacy Applications
13

14. Big Data
Intel® Distribution for Apache Hadoop* Software
Optimized Software Stack
• Stable, enterprise-ready Hadoop* • Optimized for Intel® Architecture
• Bring “Real-time” analysis to Hadoop by HBase* • Enhanced features to Hadoop for vertical
enhancements segments
Intel® Manager for Apache Hadoop Software 2.3
Deployment, Configuration, Monitoring, Alerting and Security
Mahout* 0.7 R - statistics Hive* 0.9.0 Pig* 0.9.2 Oozie* 3.3.0
Sqoop* 1.4.1
RDB Data Collector
Data Mining Data Manipulation Data Warehouse Data Manipulation Workflow Scheduler
ZooKeeper* 3.4.5
MapReduce 1.0.3
Distributed Processing Framework
Coordination
HBase 0.94.1
Flume* 1.3.0
Log Data Collector
Real-time Distributed Big Table
HDFS* 1.0.3
Hadoop Distributed File System
14

15. Intel® Distribution for Apache Hadoop* Software
Enhancements for Intelligent Transportation System
Enhancement Benefit for ITS
Cross-site Big Table for HBase* • Data are stored in different region data center
with a global virtual view
• Each data center is the live backup to provide
data access high availability
SQL Layer on top of HBase • Real-time statistics on the big mount of traffic
data
• The interactive query and offline statistic share
the same set of data
Full-text indexing and near-real- • Provide the full text search capability on the
time search for HBase structured data in distribution database system
• Build in index make sure that the traffic data
always synchronize with the index
Efficiently Big Object Storage in • Increase the traffic image store performance
HBase with the standard HBase interface
R language statistics support to • Brings the mature R language library to the
Hadoop* MapReduce, HDFS* and HBase
• Reduce the effort to develop the complex data
mining logic
15

16. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
16

17. Value for Edge Analytics
Video created
Video analyzed
Video Cold storage
Video metadata stored
Camera
Video Storage
(Edge or
Private Cloud Public Cloud
Centralized)
Management
Police System
Car
Video
Edge Client
Indexer/Analyzer/Transcoder Data Center/ Cloud Data Services
(Video capture)
(Image extraction & Metadata Creation) (Private/Public) (VSaaS, VAaaS)
Smart
Checkpoint District City  Province  PRC
1 Edge VA’s Value By end of 2017
By end of 2017 • Real-time intelligence (into metadata) 76 PB
457 PB raw video 1/6 of video data
Metadata
for traffic • Reduce the footage to be 1/8 ~ 1/12
Per Day
generated per day of its original size
• Resolve the bandwidth issue and People, cars,
backend storage capacity constrain license plates
1 Source: Internal Team Analysis based on IHS* Research Report
17

18. Enhanced NVR (Network Video Recorder)
Key Features for Intelligent Transportation
System (ITS)
闯红灯// Run the 车牌颜色识别 // Plate
red light colour recognition
逆行 // 车身颜色识别 // Vehicle
Retrogradation colour recognition
车牌识别 // Plate 交通拥堵 // Traffic jam
Recognition
车流统计// Vehicle 行使缓慢 // Run slowly
counting
禁停// No parking 行使超速 // Speeding
禁左禁右 // No turn 行人横穿//Jaywalk Crossing vehicle
right or left capture
占道(不按规定车道行使) 车标识别//Auto logo
recognition
变道 // Lane 机动车抓拍 //
Change Abnormality quick
shot
压线 // Line 超速 // Speeding
crossing Vehicle features
recognition
18

19. Intel® Architecture Base NVR
Intel® Xeon® Processor E3
*
Family, Intel® C216 Chipset
-Up to 32G DDR3 Memory
-4 x 10M/100M/1000M Base-T LAN
-16 x SATA3.0 or 24 x SATA3.0
-2 x MSATA;
-3U or 4U rack-mounted Chassis
3rd Generation Intel®
*
Core™ Processor Family
- 4 x 8G DDR3 Memory
- 2 x 10M/100M/1000M Base-T
LAN
- 8 x SATA3.0 or 16 x SATA3.0
- 1 x MSATA
- 2U or 3U rack-mounted Chassis
Intel® Atom™ Processor D2550,
Intel® NM10 Express Chipset
- 1 x 4G DDR3 Memory
*
- 2 x 100M/1000M LAN
- 8 x SATA3.0;
- 1 x MSATA;
- 2U rack-mounted Chassis
19

20. Big Data Appliance Reference Intel® Server Board
Design from Intel S2600GZ “Grizzly Pass”
Intel® Server System
R2000 “Big Horn Peak”
HDFS* Data Node
• Large Storage Capacity
• Large Memory Capacity
• Extreme Power Efficiency
• Extreme FDR InfiniBand
• Extensive I/O
• Optional SSD or PCI Express* SSD
InfiniBand*
and Ethernet
Switches
Intel Server Board
S2600JF “Jefferson Pass”
Intel Server System
H2000 “Bobcat Peak”
HDFS Name Node
• High Density Form Factor
• High Memory Bandwidth
• Extreme FDR InfiniBand
• Optional SSD or PCI Express SSD
Can be data node for
compute-intensive Big Data applications
20

21. Big Data Appliance Reference Design:
Turnkey Platforms for ISV/SI/LOEM
Easy to Use Quality
• Easy to deploy • Integrated validation of all
• Easy to scale-out components
• Easy to manage • OS and device drivers
• Rapid deployment in days • Big Data software packages
• Quickly isolate root cause • BIOS, firmware, etc.
between appliance and • Embedded acceptance test
application • Disk health monitoring
Power Efficiency Performance
• Spread core design • 10GbE, InfiniBand*
• Cold Redundant Power Supply • Advanced storage controller
• Intelligent disk spin-up/off • SSD and PCI Express* SSD
• ACPI* S3/S4 support • SW tuning: block size, # of
• DCM integrated at rack reducers, etc.
Big Data ISV/SI/LOEM all look forward to a total solution
21

22. Big Data Appliances Reference Design from Intel®
Performance & Power Advantages
• 10GbE & InfiniBand* FDR 80 PLUS 230V Internal
Certification Redundant
• Network protocol advances % of Rated Load 20% 50% 100%
• SSD, PCI Express* SSD, Hybrid 80 PLUS Bronze 81% 85% 81%
80 PLUS Silver 85% 89% 85%
• Advanced storage controller 80 PLUS Gold 88% 92% 88%
• Balance oriented optimization 80 PLUS Platinum 90% 94% 91%
Low Power Technology Sources
CRPS (5-10% up)
Power Supply: 80 PLUS Platinum HW
Power Supply: “cold” redundancy HW
Utilization (%)
Spread-core server board layout HW
Normal
ACPI S3 support HW-SW PSU
ACPI S4 support HW-SW 1 module 2 modules
Staggered disk spin up HW-FW works work
Intelligent disk spin off control HW-SW
Load (%)
Data center, rack, and node level Threshold (40%)
HW-FW-SW
power monitoring and limiting Cold Redundancy Power Supply
(CRPS)
22

23. Agenda
• Intelligent Transportation System (ITS) landscape in
China
• Blueprint for ITS
• Big Data overview and benefit for ITS
• Intel® Architecture based products for Big Data on
ITS
• ITS case study in China
23

24. Case Study:
Intelligent Monitoring and Recording System
Traffic Flow
Analysis
Vehicle
History
Hive* Behavior
Edge Video Analytic MapReduce
(Enhanced NVR)
ETL
3G
HBase*
Real-time Vehicle
HDFS* License Analysis
24

25. Case Study: Intelligent
Transportation System (ITS) Solution
Traffic Management
• Real-time road conditions report
• Over speed vehicles detection in
road segment
• Fake plate number detection
Public Security
• Tracking vehicle in real-time
• Alerts and alarms based on
blacklist
Traveler Guide
• Real-time road condition by
getting latest camera images and
traffic flow statistics
• Travel time estimation for road
segments in the city
25

26. Case Study: Intelligent
Transportation System (ITS) Result
Illegal vehicle tracking efficiency
违法车辆追踪效率提升
Through the massive data real-time analysis function, the
通过海量数据实时分析处理功能能将违法车辆数据定位时间由小时
illegal vehicle location data time is by the hour Level
级缩减为分钟级甚至秒级
reduced to minutes or even seconds.
Deaths in bad traffic accidents
恶性交通事故死亡人数减少
Through the floating vehicle monitoring system to collect vehicle
通过浮动车监控系统收集车辆信息并且实时分析，能够对事故高发车
information and real-time analysis to monitor high Service Vehicles
辆（如工程货车）进行行为监控，降低恶性事故率。
(such as engineering truck ) behavior and reduce the accident rate.
Road congestion rate
道路拥堵率下降
通过路况监控设备收集路况信息并实时处理，能够精确绘制道路拥堵线
Through the traffic monitoring equipment to collect traffic information
and real-time processing, the road congestion coil can be drawn
圈，提供交管部门快速处理突发事故，并提供给大众平台供驾驶员参考
accurately, emergency can be routed to traffic management
从而疏导车流
departments rapidly and traffic drivers can be diverted accordingly.
26

27. Summary
• Intelligent Transportation System (ITS) is
Intel® global focus now and future
• Intel®’s end-to-end analytics architecture
fits ITS solution development
• Intel® has rich resources to help developers
for ITS related application development
27

28. Additional Sources of Information:
Other Sessions
• EMBS002
‒ Real Time Cloud Infrastructure and Virtualized Data Plane Design with Intel®
Architecture: April 10, 14:30 in Room 307A
• EMBL001
‒ Hands-on Lab: Next Generation Firewall and Deep Packet Inspection on Intel®
Platforms: April 10, 13:15 in Room 306A
• EMBL001R
‒ Repeat of Hands-on Lab: Next Generation Firewall and Deep Packet Inspection on
Intel® Platforms: April 10, 15:45 in Room 306A
• EMBS003
‒ Telecommunication Platforms: Streaming Media Processing on Intel® Architecture:
April 11, 15:45 in Room 307A
• EMBS004
‒ Create Intelligent Retail Solutions that Deliver Engaging User Experiences: April 11,
15:00 in Room 307A
Demos in the showcase
‒ Booth No.: E20 “Hikvision* demo”
‒ Booth No.: E40 “Intel® Server Solutions”
More web based info:
‒ DSS web link: http://www.intel.com/info/dss
‒ Server Edge: www.IntelServerEdge.com
28

29. Legal Disclaimer
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• Copyright © 2013 Intel Corporation.
29

30. Risk Factors
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could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the
following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand
could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance
of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns
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Rev. 1/17/13
30

31. Backup
31

32. Intelligent Transportation System (ITS)
200
benefits from Interactive Hive Query
159
150 100 million records
98 over a 8-node cluster
100
68 63
50 28 Hive* 0.9.0 (M/R) (sec)
18
0.2 0.2 Interactive Hive (sec)
0
Query 1 Query 2 Query 3 Query 4
User Scenario Query
Calculate each day’s internet traffic of a specific SELECT sum(down+up) FROM cdr201209 WHERE
user number = '13300000000' GROUP BY day;
Get the 10 most heavily called numbers for a SELECT TOP(10) tonumber, sum(call_length) len FROM
specific user cdr_201209 WHERE number = '13300032810' GROUP
BY tonumber ORDER BY len DESC
Get the top 1000 call length from all user phone SELECT TOP(1000) number, call_length FROM
calls cdr_201209 ORDER BY call_length DESC
Get the top 1000 users having highest total SELECT TOP(1000) number, sum(fee) f FROM
monthly charge cdr_201209 GROUP BY number order by f DESC
Intel® Distribution for Apache Hadoop*
32 Software Enhancement

33. Intelligent Transportation System (ITS)
benefits form Cross-site Big Table
Two deployment models: 1. Global Table View
1. In transportation system, 2. Data are physically stored
each district has a DC, one in geo-distributed data
can connect to any DC and centers
view all of the data
3. Higher availability
2. In banks, provincial branch Data Center 4. Better locality
has its own DC. Central bank A
can view all of the data, but 5. Distributed aggregation
branches can not see each removes data transfer
other. Virtual
Big Table
Data Center
C
Data Center
B
Async Replication
Intel® Distribution for Apache Hadoop*
33 Software Enhancement

34. Intelligent Transportation System (ITS) Benefits
From HBase* Big Object Storage
Insertion Performance(Single Client, No pre-split)
Insert performance increase
250
records/second
Insertion Performance(500KB/record) 200%，insert latency
200
reduces 90%
150
100
50 *
hbase(no presplit)
hbase lob
0
0s 100s 200s 300s 400s 500s
Insertion Delay(Pre-split 32 regions, 6 Client Nodes)
Delay:
120
Insertion hbase*lob delay/s
Test setup: (intel-01 cluster, 6 machines, E5- 100
hbase delay/s
2620, 24core, 48G memory).
80
No client cache, No WAL. For HBase* (no
split), after insertion, the region count is 20. 60
40
20
0
0s 200s 400s 600s 800s 1000s 1200s
Intel® Distribution for Apache Hadoop*
34 Software Enhancement