Computer Science / Computer Vision

Computer Vision for Embedded Systems

Brought by: edX

Overview

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.

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.

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

Taught by

Yung-Hsiang Lu

Computer Science / Computer Vision

Computer Vision for Embedded Systems

Computer Vision for Embedded Systems

Brought by: edX

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