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.
Fathi and Mori REF developed a method to extract mid-level motion features by using the low-level optical flow for action recognition.
Action recognition by learning mid-level motion features
2008 IEEE Conference on Computer Vision and Pattern Recognition
2008 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science