Machine Learning and Computer Vision

Research Description 

We are developing machine learning algorithms to automatically track, model, and control multibody dynamic systems. Our efficient algorithms are deployed on mobile devices and control hardware units. Applications include markerless tracking of human movements, control of autonomous vehicles, automotive powertrain models, and environment recognition systems for lower-limb exoskeletons and prostheses.

Student Researchers 

William McNally
Yuan Lin
Brock Laschowski 
Arash Hashemi
Chris Shum 

Keywords and Themes 

• Deep Learning and Computer Vision
• Convolutional Neural Networks
• Reinforcement Learning
• Autonomous Vehicles
• Environment Recognition Systems


Related Publications 

• Lin Y, McPhee J, and Azad NL. (2019). Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control. arXiv:1910.12047  
• McNally W, Wong A, and McPhee J. (2019). STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection. arXiv:1902.10024.
• McNally W, Vats K, Pinto T, Dulhanty C, McPhee J, and Wong A. (2019). GolfDB: A Video Database for Golf Swing Sequencing. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:1903.06528.