Real-time model-based video stabilization for microaerial vehicles

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.Peer ReviewedPostprint (author's final draft

Automatic Feature-Based Stabilization of Video with Intentional Motion through a Particle Filter

Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorith

Content-Preserving Warps for 3D Video Stabilization

We describe a technique that transforms a video from a hand-held video camera so that it appears as if it were taken with a directed camera motion. Our method adjusts the video to appear as if it were taken from nearby viewpoints, allowing 3D camera movements to be simulated. By aiming only for perceptual plausibility, rather than accurate reconstruction, we are able to develop algorithms that can effectively recreate dynamic scenes from a single source video. Our technique first recovers the original 3D camera motion and a sparse set of 3D, static scene points using an off-the-shelf structure-frommotion system. Then, a desired camera path is computed either automatically (e.g., by fitting a linear or quadratic path) or interactively. Finally, our technique performs a least-squares optimization that computes a spatially-varying warp from each input video frame into an output frame. The warp is computed to both follow the sparse displacements suggested by the recovered 3D structure, and avoid deforming the content in the video frame. Our experiments on stabilizing challenging videos of dynamic scenes demonstrate the effectiveness of our technique

Live User-guided Intrinsic Video For Static Scenes

We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance