Investigating the impact of frame rate towards robust human action recognition

Abstract

Human action recognition from videos is very important for visual analytics. Due to increasing abundance of diverse video content in the era of big data, research on human action recognition has recently shifted towards more challenging and realistic settings. Frame rate is one of key issues in diverse and realistic video settings. While there have been several evaluation studies investigating different aspects of action recognition such as different visual descriptors, the frame rate issue has been seldom addressed in the literature. Therefore, in this paper, we investigate the impact of frame rate on human action recognition with several state-of-the-art approaches and three benchmark datasets. Our experimental results indicate that those state-of-the-art approaches are not robust to the variations of frame rate. As a result, more robust visual features and advanced learning algorithms are required to further improve human action recognition performance towards its more practical deployments. In addition, we investigate key-frame selection techniques for choosing a set of suitable frames from an action sequence for action recognition. Promising results indicate that well designed key-frame selection methods can produce a set of representative frames and eventually reduce the impact of frame rate on the performance of human action recognition.