Electronic image stabilization system based on global feature tracking

A new robust electronic image stabilization system is presented, which involves feature-point, tracking based global motion estimation and Kalman filtering based motion compensation. First, global motion is estimated from the local motions of selected feature points. Considering the local moving objects or the inevitable mismatch, the matching validation, based on the stable relative distance between the points set is proposed, thus maintaining high accuracy and robustness. Next, the global motion parameters are accumulated for correction by Kalman filteration. The experimental result illustrates that the proposed system is effective to stabilize translational, rotational, and zooming jitter and robust to local motions.

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

Electronic stabilization of catadioptric panoramic image

An effective algorithm of electronic image stabilization （EIS） of catadioptric panoramic imaging system for track robots is presented. The key techniques of this algorithm are as follows：① A model of electronic image stabilization is built by analyzing the imaging theory and the principle of EIS, and the image shift function of unwrapped panoramic image is deduced;② The relationship equation between motion estimation parameters of annular panoramic image and motion estimation parameters of unwrapped panoramic image is developed according to the constrained aspect ratio of real objects, motion parameters of annular panoramic image are firstly estimated, and then motion parameters among the image shift function are carried out according to the relationship equation;③ An excessive stabilization threshold is presented to prevent the phenomena of excessive stabilization, and the Kalman filtering is adopted to smooth the image sequences. Numerical experimental results show that this algorithm can effectively smooth out the unwanted motion and follow the intentional camera movement under certain resolutions.

Real-time digital image stabilization based on regional field image gray projection

Digital image stabilization technique plays important roles in video surveillance and object acquisition.Many useful electronic image stabilization algorithms have been studied.A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions,respectively.For the odd field image channel,motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves.Then motion compensations can be obtained after using the Kalman filter method.Finally,the odd field image is adjusted according to the compensations.In the mean time,motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame.By minimizing absolute difference between odd and even field image gray projection curves of the current frame,the inter-field motion parameters can be estimated.Therefore,the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations.Finally,the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images.Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance.In addition,image blurring can be improved.