Project

There are many topics provided in this course project. Students can collaborate as a team to complete the project. Each team should: choose a topic, complete the topic, and present the topic. The following are rules and policy of the project.

Team work

AT MOST 10 teams. Each team has 1 ~ 4 members.

Project topic : choose only 1 topic from the following

OpenCV (6 topics)
Deep learning (2 topics): Human pose estimation, Holistic tracking.
Medical image (3 topics): Fundus image segmentation, OCT layer segmentation.
Miscellaneous (2 topics): COVID, FPGA design.
You can propose a new topic to the teacher if there is no appropriate topic for you. But you have to ask for permission from the teacher.

Requirement

Each team has to choose one topic and complete the followings
- Paper reading
- Program code writing
- Data experimenting
- Oral presentation (10~15 minutes)
- Written report (10 ~ 30 pages)

Schedule

Week 12, 5/19 : Project announcement
Week 14, 6/1 : Confirmation of team members and project title (Excel)
Week 15, 6/8 : Project proposal, 1~5 pages, docx (Template).
Week 17, 6/23 : Project presentation, 10~15 minutes (ppt)
Week 18, 6/30 : Project report, 10~40 pages, docx (Template)

OpenCV

Goal

Complete a complex opencv project with C/C++.

Description

There are 6 OpenCV projects from the book
- Mastering OpenCV 3, 2nd, by D.L. Baggio, S. Emami, D.M. Escrivá, K. Ievgen, J. Saragih, R. Shilkrot. Packt Pub., 2017. PDF (restricted access, password), Source code (GitHub).
You have to choose at least one chapter of the book as your team project.
- Cartoonifier and Skin Changer for Android (Chapter 1)
- Marker-based Augmented Reality on iPhone or iPad (Chapter 2)
- Number Plate Recognition Using SVM and Neural Networks (Chapter 3)
- Non-rigid Face Tracking (Chapter 4)
- 3D Head Pose Estimation Using AAM and POSIT (Chapter 5)
- Face Recognition using Eigenfaces or Fisherfaces (Chapter 6)

Requirement

You have to read the book chapter of the program code.
You have to successfully compile and execute the code.
You have to experiment the code with your images, data, and different parameters.
You have to extend the code with at least EXTRA USEFUL 50 lines.
You have to extend the code with more OpenCV functions not used in the program codes: At least two functions or two algorithms.

Human Pose Estimation

Goal

Complete a human pose estimation project.

Description

Recently human pose estimation can be successfully solved by deep learning. Two very successful algorithms are OpenPose (by CMU) and BlazePose (by Google).
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, ARXIV v2 2018.
- BlazePose: On-device Real-time Body Pose tracking, CVPR, 2020.
You can use 3rd-party library to complete the project. Suggested libraries are OpenVINO and MediaPipe.
You can use C/C++ to complete this project, but Python is better.
Suggested readings
- An Overview of Human Pose Estimation with Deep Learning - An introduction to the techniques used in Human Pose Estimation based on Deep Learning. 2019
- 六种人体姿态估计的深度学习模型和代码总结，2018.
- Openpose 人體姿態辨識 2020.
- Google MediaPipe for Pose Estimation, Github, 2021.
- Build a personal AI Trainer, Youtube, 2021.
- Multi-Person Pose Estimation in OpenCV using OpenPose, 2018.

Requirement

You have to complete a human pose estimation project with either OpenPose or BlazePose.
You have to experiment the code with your images, videos, and different parameters.
You have to extend the code for some applications, for example Build a personal AI Trainer.

Holistic Tracking

Goal

Complete a face/hand/body tracking project. The project may need to be implemented in embedded hardware.

Description

Recently face, hand, and body tracking can be successfully solved by deep learning. Usually an algorithm can apply in the tracking of only one object. Holistic tracking targets tracking all three objects: human face, human hand, and human body with only one algorithm.
- Simultaneously detecting face, hand motion, and pose in real-time on mobile devices
You can use 3rd-party library to complete the project. Suggested libraries are OpenVINO, DepthAI, and MediaPipe.
You can use C/C++ to complete this project, but Python is better.
You can use PC to complete this project, but embedded hardwares such as Raspberry Pi, nVidia Jetson, and Intel Neural Compute Stick are also feasible.
You may need a camera to do real-time tracking. You can use USB camera or OpenCV AI Kit (OAK).
You need to import MediaPipe deep learning models into DepthAI. Here are some tutorials:
- Hand pose : 从0到1学习使用DepthAI-手势识别
  - Hand tracking with DepthAI : Running Google Mediapipe Hand Tracking models on DepthAI hardware (OAK), GitHub.
- Human pose - 从0到1学习使用DepthAI-人体姿势跟踪检测
  - Blazepose tracking with DepthAI : Running Google Mediapipe body pose tracking models on DepthAI hardware (OAK), GitHub.

Requirement

You have to complete a holistic tracking with at least two objects.
You have to experiment the code with your images, videos, and different parameters.
You have to extend the code for some applications.

Fundus Image Segmentation

Goal

Complete a medical image segmentation project for fundus images.

OCT Layer Segmentation

Goal

Complete a medical image segmentation project for OCT (Optical Coherence Tomography) images.

FPGA Design for Computer Vision

Goal

Understand some basic concepts of implementing computer vision by Verilog.

Readings

H. Jeong, Architectures for Computer Vision: From Algorithm to Chip with Verilog. John Wiley & Sons, 2014. PDF (restricted access, password)

Requirement

You have to read the book (chapters 1~4), write some example codes, and write a report.

COVID

Goal

Learn some computer vision techniques applied for COVID-19.

Readings

Requirement

You have to read the articles, write some example codes, and write a report.

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