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In this work, we propose to employ multi-channel correlation filters for recognizing human actions (e.g. waking, riding) in videos. In our framework, each action sequence is represented as a multi-channel signal (frames) and the goal is to learn a multi-channel filter for each action class that produces a set of desired outputs when correlated with training examples. The experiments on the Weizmann and UCF sport datasets demonstrate superior computational cost (real-time), memory efficiency and very competitive performance of our approach compared to the state of the arts.