Detecting human pose in a video is a difficult task. Although many high-performed human pose estimation models have been proposed in the last few years, the estimation accuracy has always been a major concern. In this study we present a method to improve the accuracy of human pose estimation for videos. Technically, predicted human pose is a set of time series data. Thus, by using time series correlation, human pose estimation can be performed in a better accuracy. We combine a CNN based human pose estimation model with a multiple object tracking framework to achieve this. Undetected/mis-detected body joints will be interpolated using the information from previous and following frames. As a result, our proposed method improved the accuracy of an existing CNN based human pose estimation model by reducing the number of undetected and mis-detected frames by 6.30% and 0.98% respectively.