Action recognition by learning mid-level motion features

作者：Alireza Fathi, Greg Mori · 年份：2008 · DOI：10.1109/cvpr.2008.4587735 · 被引用次数：459 · 研究领域：Human Pose and Action Recognition、Anomaly Detection Techniques and Applications、Gait Recognition and Analysis

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-the-art results are presented on a variety of standard datasets.