This paper proposes a new approach for video stabilization Most existing video stabilization methods adopt a framework of three steps, motion estimation, motion compensation and image composition Camera motion is often estimated based on pairwise registration between frames Thus, these methods often assume static scenes or distant backgrounds Furthermore, for scenes with moving objects, robust methods are required for finding the dominant motion Such assumptions and judgements could lead to errors in motion parameters Errors are compounded by motion compensation which smoothes motion parameters This paper proposes a method to directly stabilize a video without explicitly estimating camera motion, thus assuming neither motion models nor dominant motion The method first extracts robust feature trajectories from the input video Optimization is then performed to find a set of transformations to smooth out these trajectories and stabilize the video In addition, the optimization also considers quality of the stabilized video and selects a video with not only smooth camera motion but also less unfilled area after stabilization Experiments show that our method can deal with complicated videos containing near, large and multiple moving objects

/pdf/video-stabilization-using-robust-feature-trajectories-3z1dpwmm2s.pdf

Video stabilization using robust feature trajectories

This paper presents a novel digital video stabilization approach that provides both efficiency and robustness. In this approach, features of each frame are first detected by the oriented features from accelerated segment test (FAST) method, and then the corresponding features between consecutive frames are matched by a very fast binary descriptor which is based on the rotated binary robust independent elementary features (BRIEF). The oriented FAST combined with the rotated BRIEF, which is called ORB, is very efficient in feature detection and matching, and can be used to speed up the motion estimation without any hardware acceleration. In addition, an improved motion smoothing method is proposed to smooth affine model based motion parameters without accumulative global motion estimation. Unlike the conventional method, the proposed method uses unstable input frames and stabilized output frames instead of original input frames to estimate motion parameters directly, allowing for more desirable motion parameters. Experiments with a variety of videos demonstrate that the proposed approach is both efficient and robust.

Fast feature-based video stabilization without accumulative global motion estimation

This paper presents a novel approach to digital video stabilization that uses adaptive particle filter for global motion estimation. In this approach, dimensionality of the feature space is first reduced by the principal component analysis (PCA) method using the features obtained from a scale invariant feature transform (SIFT), and hence the resultant features may be termed as the PCA-SIFT features. The trajectory of these features extracted from video frames is used to estimate undesirable motion between frames. A new cost function called SIFT-BMSE (SIFT Block Mean Square Error) is proposed in adaptive particle filter framework to disregard the foreground object pixels and reduce the computational cost. Frame compensation based on these estimates yields stabilized full-frame video sequences. Experimental results show that the proposed algorithm is both accurate and efficient.

Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.

/pdf/real-time-model-based-video-stabilization-for-microaerial-4veo4lrd64.pdf

Real-Time Model-Based Video Stabilization for Microaerial Vehicles

https://boa.unimib.it/bitstream/10281/127956/2/NeuroRevViz-small.pdf

Analyzing crowd behavior in naturalistic conditions

SIFT Features Tracking for Video Stabilization

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SIFT Features Tracking for Video Stabilization