Events

Louis-Philippe Morency: “Multi-View Latent Variable Discriminative Models For Action Recognition” and “3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking”

June 18, 2012 | Providence, RI

Speaker: Louis-Philippe Morency
Host: Computer Vision and Pattern Recognition (CVPR)

“Multi-View Latent Variable Discriminative Models For Action Recognition”
Abstract: Many human action recognition tasks involve data that can be factorized into multiple views such as body postures and hand shapes. These views often interact with each other over time, providing important cues to understanding the action. We present multi-view latent variable discriminative models that jointly learn both view-shared and viewspecific sub-structures to capture the interaction between views. Knowledge about the underlying structure of the data is formulated as a multi-chain structured latent conditional model, explicitly learning the interaction between multiple views using disjoint sets of hidden variables in a discriminative manner. The chains are tied using a predetermined topology that repeats over time. We present three topologies – linked, coupled, and linked-coupled – that differ in the type of interaction between views that they model. We evaluate our approach on both segmented and unsegmented human action recognition tasks, using the ArmGesture, the NATOPS, and the ArmGesture-Continuous data. Experimental results show that our approach outperforms previous state-of-the-art action recognition models.

3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking
Abstract: We present 3D Constrained Local Model (CLM-Z) for robust facial feature tracking under varying pose. Our approach integrates both depth and intensity information in a common framework. We show the benefit of our CLMZ method in both accuracy and convergence rates over regular CLM formulation through experiments on publicly available datasets. Additionally, we demonstrate a way to combine a rigid head pose tracker with CLM-Z that benefits rigid head tracking. We show better performance than the current state-of-the-art approaches in head pose tracking with our extension of the generalised adaptive view-based appearance model (GAVAM).