Existing action counting methods typically rely on pixel-based changes within videos, leading to high computational redundancy and low accuracy due to the limited spatial sensitivity. To address these challenges, we introduce MoCount, the first framework that leverages 3D motion representations for counting tasks. MoCount significantly reduces computational overhead and improves counting accuracy, benefiting from the simplicity of motion representation and strong spatial sensitivity. Specifically, we utilize a motion estimator to convert video subjects into 3D motion data. A motion encoder, combined with a Sparse Spatial-Temporal module, is then applied to extract robust human body representations, yielding precise counting results. Extensive experiments on the RepCount and UCFRep datasets show that MoCount achieves state-of-the-art performance, reducing inference latency by approximately 2-3 times compared to existing video counting models. These advantages position MoCount as a leading solution for real-world action counting applications.