Smart city
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Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there. |
Details
| Workload/ECTS Credits: | 180h, 5-6 ECTS |
| Module: | M.Inf.1222 (Specialisation Computer Networks, 5 ECTS) or M.Inf.1129 (Social Networks and Big Data Methods, 5 ECTS) or M.Inf.1800 (Practical Course Advanced Networking, 6 ECTS) |
| Lecturer: | Prof. Xiaoming Fu |
| Teaching assistant: | MSc. Fabian Wölk, MSc. Weijun Wang, Dr. Tingting Yuan |
| Time: | TBD |
| Place: | TBD |
| UniVZ | [] |
Announcement
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
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.
The goal of this course is to:
- Help students to further understand computer networks and data science knowledge.
- Help students to use computer science knowledge to build a practical AI system.
- Guide students to utilize knowledge to improve the performance of the system.
In this course, each student (max. number 30) needs to:
- Read state-of-art papers.
- Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.
- Learn how to analyze city public transport sensor data.
For the project we will design, implement, and deploy the system at several buses at specific positions with sub-systems consisting of:
- Depth camera (e.g. Intel RealSense D435)
- On-board computers (e.g. Raspberry Pi Zero, NVIDIA Jetson AGX Xavier)
- Power supply (e.g. EC Technology Powerbank)
All these sub-systems in each bus will be combined to one system which shall be deployed for ideally an initial period of 2 months, thus obtaining sufficient data patterns for further analysis.
Tasks of students and implementation plan The students will be divided into 2 groups consisting of six 2-person teams. Each group will take responsibility to reimplement (and possibly adapt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate. Note that we will give a default version of each module to guarantee the basic operation of whole system.
The main tasks are as follows: 1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically. 2. Label corresponding objects/events in videos as dataset. 3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video. a) We split the architecture into modules. Each 2-person team takes care of one module then the group combines the modules together. 4. Based on the implemented architecture, each team should develop an idea to improve the architecture. Then implement a demo, deploy in the bus system, show the collected results and present the results in the final Smart City report. a) The idea can be a new application. b) The idea can also be an algorithm or module on how to improve the performance of the architecture. The milestones maybe as follows: 1. Understand the design of overall systems and modules (04.11.2020-18.11.2020 2 weeks). 2. Reimplementation and integration in 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 analysis and implement new ideas based on system (06.01.2021-11.03.2021 13 weeks). a) 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 the 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.
Prerequisites
- You are highly recommended to have completed a course on Data Science (e.g., "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
Schedule
| When? | What? |
| TBD | TBD |