Structured Models for Human Activity Recognition

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Great strides have been made within computer vision in recent years. Accuracy and capacity for automatic interpretation on a variety of recognition problems -- e.g. object recognition, human action recognition -- have improved dramatically. This progress has been driven by novel problem definitions and representations, the resurgence of powerful machine learning algorithms, and access to large quantities of labeled training data. However, current algorithms lack the capability to produce accurate structured representations that permit detailed scene understanding. For example, reasoning about groups of people in a scene or spatio-temporal relations within a video sequence remain challenging. We propose research to push the state of the art in this area by focusing on: (1) novel deep structured models for recognition and (2) active learning algorithms for facilitating data collection and pushing performance toward 100% correct computer vision.