Computer Vision for Embedded Systems
This course provides an overview of running computer vision (OpenCV and PyTorch) on embedded systems (such as Raspberry Pi and Jetson). 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.
Upcoming start dates
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- Virtual Classroom
Who should attend?
Knowledge of Python and Data Science or similar.
- 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
- Pruning and network architecture search
- Tree modular networks
- Vision in context, MobileNet
- Real-time scheduling
Course delivery details
This course is offered through Purdue University, a partner institute of EdX.
7-8 hours per week
- Verified Track -$750
- Audit Track - Free
Certification / Credits
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.
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