Action recognition by learning mid-level motion features

This paper presents a method for human action recognition based on patterns of motion. Previous approaches to action recognition use either local features describing small patches or large-scale features describing the entire human figure. We develop a method constructing mid-level motion features which are built from low-level optical flow information. These features are focused on local regions of the image sequence and are created using a variant of AdaBoost. These features are tuned to discriminate between different classes of action, and are efficient to compute at run-time. A battery of classifiers based on these mid-level features is created and used to classify input sequences. State-of-theart results are presented on a variety of standard datasets.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Alireza Fathi, Greg Mori
• Keywords: Computer Vision, CVPR 2008, Human Action Recognition, Large-scale Features, Local Features, Mid-level Features, Mid-level Motion Features