Engineering Visual Perception for Autonomous Systems
About This Course
Autonomous systems are machines that sense, decide, and act to perform tasks safely and efficiently. Visual perception is a core capability to provide rich scene understanding for detecting objects and people, reading lanes/signs, and estimating motion and position information that supports localisation, navigation, and collision avoidance under real-time constraints.
This two-day course builds practical skills for essential visual perception in autonomous systems. In this course, learners will learn practical and foundational knowledge of computer visual perception technologies for autonomous systems, and gain hands-on experience to implement, evaluate, and optimise visual perception for autonomous systems.
What You'll Learn
- Understand visual perception techniques and their use cases in autonomous systems
- Select and evaluate visual perception pipelines on target autonomous systems platforms
- Deploy, diagnose, and optimize visual perception pipelines for autonomous systems to meet system requirements
Entry Requirements
Familiar with Python programming (basic scripting, packages, Jupyter Notebook)
Basic machine-learning knowledge (datasets, training, and inference)