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|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]
|ta=MSc. Fabian Wölk (fabian.woelk@cs.uni-goettingen.de), MSc. Weijun Wang (weijun.wang@informatik.uni-goettingen.de), Dr. Tingting Yuan (tingt.yuan@hotmail.com)
|ta=MSc. Fabian Wölk (fabian.woelk@cs.uni-goettingen.de), MSc. Weijun Wang (weijun.wang@informatik.uni-goettingen.de), Dr. Tingting Yuan (tingt.yuan@hotmail.com)
|time=Mon./Wed./Thur. 14:00-16:00 (students may be divided into 3 groups due to Corona)
|time=Wed. 14:00-16:00  
|place= Room 0.103, Institute for Computer Science
|place= mostly will be online
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=270448&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|univz= Lunivz link [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=270448&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}
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'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''
'''


==General Description==
==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.


This course covers two aspects on Smart Cities in the context of public transport: event monitoring and passenger counting.  
This course covers two aspects of Smart Cities in the context of public transport: event monitoring and passenger counting.  


The goal of this course is to:
The goal of this course is to:
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*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course  "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Course  "Machine Learning" by Stanford University) before entering this course. You need to be familiar with computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
==Grading==
* Participation: 50%
** Task 1: 10%
** Task 2: 20%
** Task 3: 20%
* Presentation: 20%
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**30 minutes of presentation followed by 15 minutes Q &A for a team with two students.
Suggestions for preparing the slides:  Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of your ideas and contributions.
All quoted images, tables and text need to indicate their source.
Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. 
* Final report: 30%
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.
The source code, data (or URL of data) and a manual should be uploaded with the report.


==Schedule==
==Schedule==
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|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|{{Hl2}} |'''Output'''
|-
| align="right" |
01.11.2020
| Register the course
|
|-
|-
| align="right"|
| align="right"|
  04.11.2020
  w1
| Lecture I: Course Setup & Smart City (Online)
| Lecture I: Course Setup [https://drive.google.com/file/d/1krd4swV3brbSAZwW4VzqVisbtu0IOp5x/view?usp=sharing] & Smart City (Online)
| No
| No
|-
|-
| align="right"|
| align="right"|
  11.11.2020
  w2
| Lecture II: Object Detection & System Architecture-Video Analytics (Online)
| Lecture II: Object Detection [https://drive.google.com/file/d/1Zw6JWEL25Czev4tyPoIuNcgNo4SAFNl7/view?usp=sharing] & System Architecture-Video Analytics [https://drive.google.com/file/d/1YdXExCJnOSpZLRY4UH1ltKWAFHW4sItJ/view?usp=sharing] (Online)
| No
|
|-
|-
| align="right"|
| align="right"|
  18.11.2020
  w3
| Task 1: run Yolo for object detection
| Warm-up
| No
| No
|-
|-
| align="right"|
| align="right"|
  25.11.2020
  w4-5
| Task 2: train Yolo with a new dataset
| Task 2 report (deadline:  30.11.2020)
|-
| align="right"|
02.12.2020
| Discussion & Task 3: Yolo for depth image
|Task 3 report (deadline:  21.12.2020)
|-
| align="right" |
09.12.2020
| Discussion & Task 3: Yolo for depth image
|
|-
| align="right"|
16.12.2020
| Task 3: Yolo for depth image
|
|-
| align="right" |
23.12.2020
| Discussion & Task 4: Yolo for different topics
| Task 4 report (deadline:  08.02.2021)
|-
| align="right"|
30.12.2020
| Holiday
|
|-
| align="right"|
06.01.2021
| Holiday
|
|
Task 1
|Report
|-
|-
| align="right"|
| align="right"|
  13.01.2021
  w6-8
| Task 4: Yolo for different topics
|Task 2
|
|Report
|-
| align="right"|
20.01.2021
| Task 4: Yolo for different topics
|
|-
| align="right"|
27.01.2021
| Task 4: Yolo for different topics
|
|-
| align="right" |
03.02.2021
| Task 4: Yolo for different topics
|
|-
|-
| align="right" |
| align="right" |
  10.02.2021
  w9-14
| Discussion & Brainstorming
| Task 3
|
|
|-
|-
| align="right" |
| align="right" |
  15.03.2021
  15.03
|  Final presentations
|  Final presentations
|
|
|-
|-
| align="right" |
| align="right" |
  31.03.2021
  31.03
|  Final report
|  Final report
|
|
|-
|-
|}
|}
The milestones may be as follows:
1. Understand the design of overall systems and modules (04.11.2020-18.11.2020 2 weeks).
2. Reimplementation and integration in the laboratory (19.11.2020-09.12.2020 4 weeks).
3. Deployment and data collection (10.12.2020-11.02.2021 9 weeks including Christmas).
4. Result in analysis and implement new ideas based on system (06.01.2021-11.03.2021 13 weeks).
(Note that there are 5 weeks overlapped with Deployment and data collection in case students need to modified their program.)
5. Final presentations (the week 15.03.2021).
6. Final reports (31.03.2021)
After this course, students will have full-stack knowledge of video analytics systems, including network programming, basic knowledge on video streaming, general knowledge of object detection, and state-of-art video analytics architecture.
Yuan4
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