Computer Vision for Embedded Systems
This course provides an overview of running computer vision (OpenCV and PyTorch) on an embedded system (Raspberry PI). The course emphasizes the resource constraints imposed by embedded systems and examines methods (such as quantization and pruning) to reduce resource requirements. Course topics:
1. Overview, image data formats, OpenCV
2. Edge detection and segmentation
3. Applications of computer vision in embedded systems
4. Datasets, bias, privacy, competitions
5. Machine learning and PyTorch
6. Performance and resources (time, memory, accuracy)
7. Object detection and motion tracking
8. Data annotation and generation
9. Quantization
10. Pruning and network architecture search
11. Tree modular networks
12. Vision in context, MobileNet
13. Real-time vision
14. Review and discussion
1. Overview, image data formats, OpenCV
2. Edge detection and segmentation
3. Applications of computer vision in embedded systems
4. Datasets, bias, privacy, competitions
5. Machine learning and PyTorch
6. Performance and resources (time, memory, accuracy)
7. Object detection and motion tracking
8. Data annotation and generation
9. Quantization
10. Pruning and network architecture search
11. Tree modular networks
12. Vision in context, MobileNet
13. Real-time vision
14. Review and discussion
ECE59500
Credit Hours:
1Learning 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:
This course provides an overview of running computer vision (OpenCV and PyTorch) on an embedded system (Raspberry PI). The course emphasizes the resource constraints imposed by embedded systems and examines methods (such as quantization and pruning) to reduce resource requirements. Course 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 vision
- Review and discussion