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Driver distraction is one of the major causes for fatal car accidents. In a distracting activity, manual distraction is a triggered response of other types of distraction, such as cognitive and visual distraction. Therefore, recognition of manual distraction can contribute to the monitoring of overall drivers’ distraction. In this study, a computer vision-based method to track hand movement from the recorded driving behavior is proposed. This method integrates a low computational cost template matching algorithm using fast normalized cross coefficient (NCC) and a novel searching strategy. The proposed method was evaluated by the VIVA hand tracking data set. It achieves 50.83% of marginal accuracy percentage (mAP), 42.18% of multiple object tracking accuracy (MOTA), 31.56% of mostly tracked (MT), and 19.29% of mostly lost (ML), and it outperformed a state of the art algorithm in MOTA and MT. Additionally, the computational cost of the proposed method is greatly improved, and it can run at around 11.1 frames per second. The outcome of this research will further assist driving distraction recognition and mitigation, and improve driving safety.