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This document proposes a smart city system using facial recognition on surveillance cameras to track criminal suspects in real-time. It outlines the current manual process police use to search surveillance footage which is time-consuming. The proposed system would use machine learning to create a facial recognition model trained on a government database. This would allow the system to automatically detect and track suspects on cameras, informing police instantly of their location. Benefits mentioned include increased safety, reduced crime rates, and time savings for both the public and law enforcement.

Smart City
Face recognition base on surveillance monitor and big data that able to track
suspects

Outline
● Introduction
● Body
Feasibility analysis.
Scientific evidence.
How generic people benefited from this idea.
● Reference
● Conclution

Idea introduction
Difference between the approach in nowaday
and the conceptual illustration

Nowaday approch
fig (1)
Current practices in electronic surveillance
1. 2. 3. 4.
Police have to access to all
surveillance cameras manually.

Nowaday approch
fig (1.1)
Current practices in electronic surveillance
1. 2. 3. 4. 1.
Determine where the suspect may
appear

Nowaday approch
fig (1.2)
Current practices in electronic surveillance
1. 2. 3. 4. 2.
Access the surveillance on the path

Nowaday approch
fig (1.3)
Current practices in electronic surveillance
1. 2. 3. 4. 3.
Capture and organize videos for a
specific time period and then
assign tasks

Nowaday approch
fig (1.4)
Current practices in electronic surveillance
1. 2. 3. 4. 4.
Watch video manually and try to
find where the suspect is.

What is the problem?
● Time consuming.
● suspect may run far away during the period of watching video
● human may fail to recognize the face with confusing by wearing different cloth
suspect.

The illustration of face recognition in
surveillance monitor
fig (2)
Using machine learning to create face
recognition system
1. 2. 3.

The illustration of face recognition in
surveillance monitor
fig (2)
Using machine learning to create face
recognition system
1. 2. 3.
Collect the face into government data
management system

The illustration of face recognition in
surveillance monitor
fig (2)
Using machine learning to create face
recognition system
1. 2. 3.
Machine learning to training facial
regonition model.

The illustration of face recognition in
surveillance monitor
fig (2)
Using machine learning to create face
recognition system
1. 2. 3. Cloud system will imform the
police where the suspect is,
once the system find the
specific person.

Interesting for General Public
Safe life Reduce crime rates

Interesting for General Public
Enegy efficiency Saving time

Interesting for CS/CE Students
The active of samrt
city bring..
Employment
Career Opportunities
More funding for research

Example of SMART CITY
technorogy
Anylsing data from
surveillance
cameras
Using for tracking offenders !
Using for preventing crime !

Importantance of IoT
● Improving convenience
● More efficiency
● cost economy

Example of current IoT
technology...
● Door lock system
● air conditioner
etc...

Importance of BIG DATA
● Build database
→ human face database
● Data analyzing
→ facial recognition

BIG DATA Management
Outline Your Goals Data Protection
NEW WAY
OLD WAY
Adapt to the
New Changes

Facial Recognition
source : ccw1986.blogspot

Short Video
https://www.youtube.com/watch?v=zMhHsaHR8oA

Challeng
e
Cheaper Device
Privacy problem
Power Saving
IoT

Future of SMART
CITIES
● More business opportunities

Refference ● Real-Time Action Detection in Video
Surveillance using Sub-Action Descriptor with
Multi-CNN
● BALANCING OF NEEDS BETWEEN :SECURITY
AND PRIVACY ENGLAND
● Identify and Trace Criminal Suspects in the
Crowd Aided by Fast Trajectories Retrieval
● Face recognition-based real-time system for
surveillance

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Home work

1. Smart City Face recognition base on surveillance monitor and big data that able to track suspects

2. Outline ● Introduction ● Body Feasibility analysis. Scientific evidence. How generic people benefited from this idea. ● Reference ● Conclution

3. Idea introduction Difference between the approach in nowaday and the conceptual illustration

4. Nowaday approch fig (1) Current practices in electronic surveillance 1. 2. 3. 4. Police have to access to all surveillance cameras manually.

5. Nowaday approch fig (1.1) Current practices in electronic surveillance 1. 2. 3. 4. 1. Determine where the suspect may appear

6. Nowaday approch fig (1.2) Current practices in electronic surveillance 1. 2. 3. 4. 2. Access the surveillance on the path

7. Nowaday approch fig (1.3) Current practices in electronic surveillance 1. 2. 3. 4. 3. Capture and organize videos for a specific time period and then assign tasks

8. Nowaday approch fig (1.4) Current practices in electronic surveillance 1. 2. 3. 4. 4. Watch video manually and try to find where the suspect is.

9. What is the problem? ● Time consuming. ● suspect may run far away during the period of watching video ● human may fail to recognize the face with confusing by wearing different cloth suspect.

10. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3.

11. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Collect the face into government data management system

12. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Machine learning to training facial regonition model.

13. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Cloud system will imform the police where the suspect is, once the system find the specific person.

14. Interesting for General Public Safe life Reduce crime rates

15. Interesting for General Public Enegy efficiency Saving time

16. Interesting for CS/CE Students The active of samrt city bring.. Employment Career Opportunities More funding for research

17. Example of SMART CITY technorogy Anylsing data from surveillance cameras Using for tracking offenders ! Using for preventing crime !

18. Importantance of IoT ● Improving convenience ● More efficiency ● cost economy

19. Example of current IoT technology... ● Door lock system ● air conditioner etc...

20. Importance of BIG DATA ● Build database → human face database ● Data analyzing → facial recognition

21. BIG DATA Management Outline Your Goals Data Protection NEW WAY OLD WAY Adapt to the New Changes

22. Facial Recognition

23. Facial Recognition source : ccw1986.blogspot

24. Short Video https://www.youtube.com/watch?v=zMhHsaHR8oA

25. Challeng e Cheaper Device Privacy problem Power Saving IoT

26. Reason of FailBudget Data collection

27. Future of SMART CITIES ● More business opportunities

28. Layout of Prestation

29. map

30. Refference ● Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN ● BALANCING OF NEEDS BETWEEN :SECURITY AND PRIVACY ENGLAND ● Identify and Trace Criminal Suspects in the Crowd Aided by Fast Trajectories Retrieval ● Face recognition-based real-time system for surveillance

Editor's Notes

#15: Q1 Why is the topic of SMART CITIES interesting for the general public?
#16: Q1 Why is the topic of SMART CITIES interesting for the general public?
#17: Q2 Why is the topic of SMART CITIES interesting for computer science/engineering students?
#18: Q2 Why is the topic of SMART CITIES interesting for computer science/engineering students?
#19: Q3 How is the IoT the most important feature of SMART CITIES?
#20: Q3 How is the IoT the most important feature of SMART CITIES?
#21: Q4 How is BIG DATA a very important feature of SMART CITIES?
#22: Q4 How can the BIG DATA be better managed in the context of smart cities?
#23: Q5 Explain a specific technology used in the smart city context.
#24: Q5 Explain a specific technology used in the smart city context.
#25: Q6 Demonstrate a short YouTube movie explaining the ongoing research and accomplishments on the topic.
#26: Q7 Why is the research on the issue so challenging and difficult?
#27: Q7 Why do most smart cities fail across the world?
#28: Q8 What research needs to be done in the future on SMART CITIES?
#29: Q9 Show how you have used the MIND MEISTER software to plan the layout of the presentation.

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