i. Use computer vision to analyze images.

ii. List the constraints of embedded systems.

iii. Explore design space of computer vision.

iv. Evaluate different methods for accuracy/time tradeoffs.

Lecture topics:

• Overview, image data formats, OpenCV
• Edge detection and segmentation
• Applications of computer vision in embedded systems
• Datasets, bias, privacy, competitions
• Machine learning and PyTorch
• Performance and resources (time, memory, accuracy)
• Object detection and motion tracking
• Data annotation and generation
• Quantization
• Pruning and network architecture search
• Tree modular networks
• Vision in context, MobileNet
• Real-time scheduling

Fall 2021 course feedback:

• The organization of the content is superb.
• It was a very innovative class. It was refreshing that this class was focused on learning, rather than only testing the students.
• I think the concepts are delivered very well.
• I enjoyed having the exposure to quantization.
• The project structure was great in my opinion.

Spring 2022 course feedback:

(Dr. Lu gave the short course at the Seoul National University in South Korea)

• Instruction for the assignments is clear and explicit, and these assignments help me to fully understand the contents learned in the lecture.
• Lectures were very well done.
• I can feel the professor prepared the lecture well.
• Good quiz, good lecture, good lecturer
• Everything was perfect.