133
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Smart city: Difference between revisions
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The main tasks are as follows: | The main tasks are as follows: | ||
1. Collect the video data of the depth cameras with a predefined interface or preinstalled SD card periodically. | 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. | 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. | 3. Reimplement existing video analytics architecture (using open source code from papers) with collected depth image video. | ||
(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. | 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. | 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. | b) The idea can also be an algorithm or module on how to improve the performance of the architecture. | ||
The milestones maybe as follows: | The milestones maybe as follows: | ||
1. Understand the design of overall systems and modules (04.11.2020-18.11.2020 2 weeks). | 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). | 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). | 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). | 4. Result 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). | 5. Final presentations (the week 15.03.2021). | ||
6. Final reports (31.03.2021) | 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. | 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. | ||