2. M.Tech Technical seminar Presentation
Department of Information Science
R V College of Engineering, Bengaluru

3. “Intelligent Video Analytics for Smart cities”
R V College of Engineering Bengaluru
“Intelligent Video Analytics for Smart cities” By
Manjunath Reddy.V
1RV16SSE09
Under the guidance of
Dr.Shantharam Nayak
Professor
Dept. of ISE, RVCE.

5. INTRODUCTION
Video analytics is a powerful tool that has the potential to convert unstructured video data into structured useful data which can be analyzed, searched, and managed to create a real-time intelligent response system.
The introduction of intelligent video analytics in Smart City applications also comes with some challenges, such as scalability, reliability, speed, and cost-effectiveness.

7. LITERATURE SURVEY Xavier Sevillano, Virginia Fernandez-Arguedas
Sl No
Year
Authors and Journal
Name Of Topic
Features 1.
2015
Xavier Sevillano, Virginia Fernandez-Arguedas
(IEEE)
Towards Smart Traffic Management Systems:
Vacant On-Street Parking Spot Detection Based on Video Analytics 2
2017
Shrikant Jadhav, and Clay S. Gloster Youngsoo Kim, Marcus Garcia, Alan Chen, Nathan Wong (IEEE)
Smart City Service Acceleration on FPGAs
In this paper, they developed embedded controls, and FPGA design accelerating many Smart city server side programs.

8. Hrishikesh Venkataraman and Rolf Assfalg 3
2017
Hrishikesh Venkataraman and Rolf Assfalg
(IEEE)
Driver Performance Detection & Recommender System in
Vehicular Environment using Video Streaming Analytics
This paper deals with how the overall human performance while working can be detected, under different conditions and scenarios. 4
2016
Honghai Liu, Shengyong Chen , Naoyuki Kubota,
Member, (IEEE)
Intelligent Video Systems and Analytics: A
Survey (Base Paper)
This paper provides a
comprehensive account on theory and application of intelligent video systems and analytics

9. Review of Literature
Traditional video surveillance, a watch-stander often faces the duty to stare hundreds of screens.
IVS embeds computer vision technologies into video devices
IVA helps government public and commercial organizations to transform video surveillance into a real-time, proactive, event-driven process.
Alert conditions may be set with real-time processing algorithms

10. System Architecture

11. Distributed Architecture
Workload of the server would be distributed among the edge devices and the servers
Pre-configuring of servers to certain conditions to raise alarms
Existing video analytics frameworks need to be re-architectured in such a way that server processing is distributed to reduce the load on any particular server

12. Video Systems 1) Video System Architecture
Intelligent video systems and services incorporate hardware system integration, management, and video processing to end-point users.
IVS requires analytic processing in either embedded cameras or central servers

13. 2) Video Quality Diagnosis:
Self-awareness of video quality provides a means of diagnosis and alarm for system maintenance
3) System Adaptability:
System configuration include sensor planning, data fusion, and communication among multiple sensors.
4) Data Management and Transmission
Develop efficient methods for management and
retrieval of video segments based on the semantic content.

14. Analytical Methods
Intelligence: IVA have attempted to apply all adaptive and intelligent methods of neural network, knowledge-based approaches, particle filtering, finite state automation, self-organizing maps, support vector regression, semantic analysis, Markov models, decision tree, clustering etc.
Cooperative and View Selection: In multi-camera systems, there is a problem of selecting the right view to display among the multiple video streams so a view is defined by the camera index and the parameters of the image cropped within the selected camera.

15. Integration and Statistics: Statistics may be applied for event detection, counting, routing, guidance, surveillance, and flow control and Integration of information from multiple sources or cameras is necessary to make the system more intelligent.
Networked Analytics: Many Applications perceive visual information through networks of embedded sensors and distributed smart cameras perform real-time computer vision.
Learning and Classification: These are powerful for object detection and event recognition. An adaptive learning method is used to estimate the location and moving speed of a person. Hierarchical decision tree are explored for human action recognition.

16. Applications of IVA in Smart City
Management
Traffic Control and Transportation
Intelligent Vehicle
-Pedestrian detection
-Driver-assistance system
Traffic sign detection and recognition

18. Conclusion
For cost-effective analytics, distributed architecture with user control seems to be a good solution. This video analytics platform identifies the most accurate algorithms depending on the configuration, and is able to report the results and create custom alerts in a pre-configured way to attain better accuracy.

19. REFERENCES
[1] “Intelligent Video Systems and Analytics” Honghai Liu, Senior Member, IEEE, Shengyong Chen *, Senior Member, IEEE, Naoyuki Kubota, Member, IEEE, vol. 8, no. 1, p. 90, June.2016.
[2] “Smart City Service Acceleration on FPGAs” Youngsoo Kim, Marcus Garcia, Alan Chen, Nathan Wong Shrikant Jadhav, and Clay S. Gloster ,2017 IEEE Third International Conference on Big Data Computing Service and Applications, March 2017
[3] “Driver Performance Detection & Recommender System in Vehicular Environment using Video Streaming Analytics “,Hrishikesh Venkataraman and Rolf Assfalg,2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) Sheraton Arabella Park Hotel, Munich, Germany, July 3-7, 2017.

20. [4] “Towards Smart Traffic Management Systems “,Xavier Sevillano, Elena M`armol and Virginia Fernandez-Arguedas ,Volume 6, Issue 6, June (2015).
[5] H. Liu, S. Chen, and N. Kubota, "Guest Editorial Special Section on Intelligent Video Systems and Analytics," IEEE Transactions on Industrial Informatics, vol. 8, no. 1, p. 90, Feb.2012.

21. THANK YOU