Object Visual Recognition for Intelligent Vehicles
Review the motivations and methods for visual objects recognition. Focus on Machine-Learning and Machine-Learning algorithms. Consider deep-learning approaches for visual objects detection, recognition, and categorization.
What you will learn:
- Describe supervised machine-learning
- Explain "classic" machine-learning algorithms most used for visual object recognition
- Describe the originality and advantages of Deep-Learning approaches for visual objects simultaneous detection and categorization
This course is part of the following course program:
IEEE Guide to Autonomous Vehicle Technology
Courses included in this program:
Who should attend: Electrical engineer, Network engineer, Data engineer, Design engineer, Hardware engineer, Security engineer, Lead engineer, Project engineer, Product engineer
Dr. Fabien Moutarde is a Full Professor of Computational Intelligence at MINES ParisTech (PSL Université Paris). He teaches deep machine learning and teaches visual pattern recognition for intelligent vehicles at SJTU ParisTech Elite Institute of Technology, Shanghai and conducts applied research in machine learning, datamining and computer vision for mobile and/or collaborative robotics, intelligent vehicles and intelligent transport systems. Dr. Moutarde has previously worked at Alcatel-Alsthom Recherche as a R&D engineer on various applications of neural networks, and on image compression and received an engineering degree from Ecole Polytechnique (France), a PhD in astrophysics from Université Paris VII, and an Habilitation à diriger des Recherches (HDR) in Engineering Sciences from Université Pierre & Marie Curie (Paris VI).
Dr. Alexander M. Wyglinski is a Professor of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), President of the IEEE Vehicular Technology Society, and Director of the Wireless Innovation Laboratory (WI Lab). His research interests are in the area of wireless communications, connected vehicles, cognitive radios, autonomous/self-driving cars, and dynamic spectrum access networks. Dr. Wyglinski has authored/co-authored over 120 peer-reviewed journal articles and conference papers, as well as three textbooks. Dr. Wyglinski is a Senior Member of the IEEE, as well as a member of Sigma Xi, Eta Kappa Nu, and the American Society of Engineering Education (ASEE).
Publication Year: 2019