To automate video surveillance systems, algorithms must be developed to automatically detect and classify basic human actions. Many traditional approaches focus on the classification of actions, which usually assumes prior detection, tracking, and segmentation of the human figure from the original video. On the other hand, action detection is a more desirable paradigm, as it is capable of simultaneous localization and classification of the action. This means that no prior segmentation or tracking is required, and multiple action instances may be detected in the same video. Correlation filters have been traditionally applied for object detection in images. In this paper, we report the results of our investigation using correlation filters for human action detection in videos. Correlation filters have previously been explored for action classification, but this is the first time they are evaluated for the more difficult task of action detection. In addition, we investigate several practical implementation issues, including parameter selection, reducing computational time, and exploring the effects of preprocessing and temporal occlusion (i.e., loss of video frames) on performance.