Computer Vision for Embedded Systems - ECE59500
Learning Objective:
- Use computer vision to analyze images
- List the constraints of embedded systems
- Explore design space of computer vision
- Evaluate different methods for accuracy/time tradeoffs
Description:
- 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 vision
- Review and discussion
Topics Covered:
Computer Engineering, VLSI and Circuit Design
Prerequisites:
ECE20875, Python for Data Science or similar
Applied / Theory:
50 / 50
Homework:
4 homework assignments, 1 final project, class participation
Textbooks:
None
Other Requirements:
Reading materials (research papers) will be assigned. Students may purchase a Raspberry PI 4 for the programming assignments; Engineering students with ECN accounts will be able to access a Raspberry PI in the instructor's lab via the Internet.