Having high precision ground-truth data is a very important factor for the development and evaluation of computer vision algorithms such as digital video stabilization. However, generating this data is time consuming and cost intensive work, requiring a lot of manual effort. In this paper we both propose a way to automatically generate a large amount of accurate data for digital video stabilization verification and provide a comprehensive dataset of video sequences taken from multi-sensor imaging system with different types of disturbances. A novel method for generating verification data is based on genetic algorithm template matching. Paper provides quantitative analysis together with the visual assessment of digital video stabilization performance.