Structured Models for Human Activity Recognition

点击翻译按钮获取中文摘要

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.********