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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.
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Home work
The document proposes a smart city system using facial recognition on surveillance cameras to track 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 government ID photos. This would allow the system to automatically detect and alert police to a suspect's location across multiple cameras in real-time, improving efficiency over the current manual process. Concerns about privacy and costs are mentioned as challenges that would need to be addressed for such a system.
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The document proposes a smart city system using facial recognition on surveillance cameras to track 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 government ID photos. This would allow the system to automatically detect and alert police to a suspect's location across multiple cameras in real-time, improving efficiency over the current manual process. Concerns about privacy and costs are mentioned as challenges that would need to be addressed for such a system.
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overlooked or avoided by security installers unless absolutely necessary. The present focus
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Vision (CCTV) footages. This includes automatic identification of objects in a raw video,
following those objects over time and between cameras, and the interpretation of those
object’s appearance and movements. Here achieving video analytics by implementing its
segments, through Open CV with an e.g., Extracting the edges of a live video through web
cam and finding the motion detection in Live video. In this paper we even discuss about the
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IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
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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
- 23. Facial Recognition source : ccw1986.blogspot
- 25. Challeng e Cheaper Device Privacy problem Power Saving IoT
- 27. Future of SMART CITIES ● More business opportunities
- 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
- Q1 Why is the topic of SMART CITIES interesting for the general public?
- Q1 Why is the topic of SMART CITIES interesting for the general public?
- Q2 Why is the topic of SMART CITIES interesting for computer science/engineering students?
- Q2 Why is the topic of SMART CITIES interesting for computer science/engineering students?
- Q3 How is the IoT the most important feature of SMART CITIES?
- Q3 How is the IoT the most important feature of SMART CITIES?
- Q4 How is BIG DATA a very important feature of SMART CITIES?
- Q4 How can the BIG DATA be better managed in the context of smart cities?
- Q5 Explain a specific technology used in the smart city context.
- Q5 Explain a specific technology used in the smart city context.
- Q6 Demonstrate a short YouTube movie explaining the ongoing research and accomplishments on the topic.
- Q7 Why is the research on the issue so challenging and difficult?
- Q7 Why do most smart cities fail across the world?
- Q8 What research needs to be done in the future on SMART CITIES?
- Q9 Show how you have used the MIND MEISTER software to plan the layout of the presentation.