Learn about constraints and reducing resource requirements for computer vision on embedded systems. This course provides an overview of running computer vision (OpenCV and PyTorch) on embedded systems (such as Raspberry Pi and Jetson).
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The course emphasizes the resource constraints imposed by embedded systems and examines methods (such as quantization and pruning) to reduce resource requirements. This course will have programming assignments and projects proposed by the students.
Required texts or technologies:
This course does not have a required text. The course will read recently published papers. Students will use Google Colab for programming assignments.
What you'll learn
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.
Syllabus
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
