Video stabilisation based on modelling of motion imaging

Abstract

This study proposes a novel digital video stabilisation scheme based on modelling of motion imaging (MI). The modelling of MI eliminates the speed motion as a result of a moving car, which is ignored in other models such as rotation + translation model, and estimates movement parameters of the background in video sequences captured from cameras mounted on moving cars. The authors first analyse the MI to understand the principle of the effects of car motion on MI, and select the matching method according to the proposed model. Then, they employ symmetric points to remove the speed motion. Finally, unwanted motion vector is stimulated by employing adaptive step-length filter, and the boundary compensating approach is employed to suppress the image jitter effectively. Their major contribution is the elimination of the effect of carrier's speed in motion estimation. Other contributions include new robust block matching approach and adaptive-step selection for motion filtering. They conduct experiments on real videos and artificial data. Experiments on real videos show that the proposed model can remove the effect of car motion, whereas the experiments on artificial data are conducted for theoretical analysis.

Stability Precision Error Correction of Photoelectric Detection by Unmanned Aerial Vehicle

“…Parametric model (PM) is established by using general movement theory and previous analysis results of ECS in Section 2. The target position in the base coordinate CðbÞ can be calculated by (10), where P pgi is one of the sample values of the target position in the CðpgÞ coordinate at the end of the coordinate series, and P bi = ½x bi , y bi , z bi is that in the CðbÞ coordinate at the begging of the series. By doing that, the real-time target position in the absolute coordinate can be obtained just by using the gyroscope signals.…”

“…For example, Wu et al [9] studied the device stabilization with total warping variation model. Yu et al [10] studied video stabilization based on the modeling of motion imaging. Mao et al [11] proposed the continuous second-order sliding mode control based on disturbance observer for LOS stabilization.…”

Improving the robustness of motion vector temporal descriptor

“…Temporal features are also employed to obtain key frames by dividing a shot into segments of equal cumulative motion activity using MPEG-7 motion activity descriptor [1]. Moreover, temporal features are used extensively in compressed domain video for analysis and indexing applications [14][15][16][17][18][19][20][21][22]. In [7], motion information of a video is modelled by a two-dimensional motion histogram of MVs.…”

“…The algorithm utilises the motion information from the H.264 compressed bit stream to identify background motion model and moving objects. Yu et al [20] proposed a pioneering motion estimation approach based on modelling of motion imaging to stabilise the captured video from a fast-moving car. Their method was based on MVs.…”