Evaluating the impact of frame rate on video based human action recognition

Authors:
Fredro Harjanto;Zhiyong Wang;Shiyang Lu;David Dagan Feng
Affiliations:
The University of Sydney, Australia;The University of Sydney, Australia;The University of Sydney, Australia;The University of Sydney, Australia
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

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Abstract

Human action recognition in videos is one of the classic problems in computer vision domain due to its wide range of applications as well as its challenges. Although most existing approaches perform very well for specific datasets, there is little research on how practical and robust it is to extend those approaches into realistic scenarios where videos are often acquired with different frame rates from diverse imaging devices. In this paper, we investigate and evaluate recognition performance of four state-of-the-art human action recognition approaches across two widely used benchmark datasets with three different frame rate settings. It is observed in our comprehensive experiments that frame rate does affect recognition performance. Particularly, the impact of frame rate is not consistent across different scenarios. Therefore, better recognition approaches, including novel visual features and learning algorithms robust to frame rate variation, are demanded to further advance human action recognition towards more practical deployment.