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Smart city (view source)

Revision as of 18:05, 25 October 2020

633 bytes removed ,  25 October 2020
→‎Schedule
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  11.11.2020
  11.11.2020
| Lecture II: Object Detection & System Architecture-Video Analytics (Online)
| Lecture II: Object Detection & System Architecture-Video Analytics (Online)
| Papers (release 10 choose 20
| Papers (release 10 choose 2)
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  25.11.2020
  25.11.2020
| Task 1: (Vision Group) train Yolo with a new dataset
| Task 1: (Vision Group) train Yolo with a new dataset
Task 1: (System Group)  Save Realsense [https://www.intelrealsense.com/depth-camera-d435/ Realsense] camera captured image on Jetson nano
Task 1: (System Group)  efficient store image from [https://www.intelrealsense.com/depth-camera-d435/ Intel Realsense] camera on Jetson nano
| Task 1 report (deadline: 30.11.2020)
| Task 1 report (deadline: 30.11.2020)
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  02.12.2020
  02.12.2020
| Discussion & Task 2: Yolo for depth image
| Discussion & Task 2: (Vision Group) Yolo for depth image
|Task 2 report (deadline: 21.12.2020)
Discussion & Task 2: (System Group) dynamic object detection pipeline configuration adjustment
|Task 2 report (deadline: 21.12.2020)
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  13.01.2021
  13.01.2021
| Task 3: Yolo for different topics
| Task 3: (Vision Group) Yolo for different topics
| Task 3 report (deadline:   08.02.2021)
| Task 3: (System Group) object detection pipeline configuration for different topics
| Task 3 report (deadline: 08.02.2021)
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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.
Wwang
100

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