Abstract: The quality of vehicle records was seriously influenced along with the bumping and shaking driving, and the jittering videos would impact observer reading and interpreting the information. In order to improve the quality of the records, a real-time image stabilization method for vehicle cloud platforms had been proposed. Firstly, the nonlinear filter was adopted to build scale space for the purpose of highlighting edge information; Secondly, a feature detection combined fast image brightness test and gradient calculation was put forward for more quality features, furthermore, increasing the self-salience and differences between descriptors brought discriminative power; Finally, similar features was availably distinguished by location verification for accurate estimation of global motion vector. The time experimental results shown that the proposed approach fulfile the task of real-time, and the average frame rate is over 60 even when the resolution is 720 P. In effective capability experiments, the repeatability of new method is more than 65%, which indicates the enhancement of detection power. Furthermore, the ITF of 6 group tested increase 46.8%, 30.8%, 28.44%, 28.1%, 33.9% and 53.4% after dejitter design, which illustrates that the method significantly improves the effectiveness and precision of the vehicle image stabilization algorithm.