Real-time Video Stabilization Research Articles

Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.

General Digital Image Processing Circuit and its Applications

In this paper, the design and architecture of general digital image processing circuit (GFDIPC) based on a Xilinx Virtex-5 FPGA processor is presented. The GFDIPC applications on thermal imaging, image fusion, real-time video stabilization and time-delay test for image processing system are described. The design of FPGA in each application follows the principle of functional modularity, pipeline processing and parallel computing, which brings FPGA high efficiency and high real-time performance.

Real-Time Video Stabilization Based on Smoothing Feature Trajectories

In this paper, a real-time video stabilization algorithm based on smoothing feature trajectories is proposed. For each input frame, our approach generates multiple feature trajectories by performing inter-frame template match and optical flow. A Kalman filter is then performed to smooth these feature trajectories. Finally, at the stage of image composition, the motion consistency of the feature trajectory is considered for achieving a visually plausible stabilized video. The proposed method can offer real-time video stabilization and its removed the delays for caching coming images. Experiments show that our approach can offer real-time stabilizing for videos with various complicated scenes.

Real-Time Video Stabilization Based on Smoothing Feature Trajectories

In this paper, a real-time video stabilization algorithm based on smoothing feature trajectories is proposed. For each input frame, our approach generates multiple feature trajectories by performing inter-frame template match and optical flow. A Kalman filter is then performed to smooth these feature trajectories. Finally, at the stage of image composition, the motion consistency of the feature trajectory is considered for achieving a visually plausible stabilized video. The proposed method can offer real-time video stabilization and its removed the delays for caching coming images. Experiments show that our approach can offer real-time stabilizing for videos with various complicated scenes.

Design and Implementation of Efficient Video Stabilization Engine Using Maximum a Posteriori Estimation and Motion Energy Smoothing Approach

To smooth the video content caused by handheld devices, this paper designs a hardware-oriented engine for efficient video stabilization. This engine is realized based on the motion energy computation. Maximum a posteriori estimation derives the global motion. Significantly, the motion energy smoothing accomplishes video stabilization. The global motion is smoothed by calculating the continuous and curve energy of successive frames. In addition, to achieve real-time video stabilization, efficient hardware architecture is proposed. The novel data reuse scheme is designed for enhancing the speed of corner point detection. The estimation skip technique is manipulated for lowering the computation of local motion estimation. Double buffering and pipeline running is designed for efficiently deriving the global motion. With these approaches, the corresponding hardware architecture has the characteristics of high efficiency and high throughput. The experimental results show that the proposed video stabilization engine can produce well-smooth videos and have high precision. The proposed hardware architecture enhances the performance of video stabilization with real-time and large resolution-processing ability. The objective comparison also demonstrates our good performance on video stabilization.

Adaptive Optics Scanning Laser Ophthalmoscope-Based Microperimetry

To develop and test the application of an adaptive optics scanning laser ophthalmoscope (AOSLO) with eye tracking for high-resolution microperimetric testing. An AOSLO was used to conduct simultaneous high-resolution retinal imaging and visual function testing in six normal subjects. Visual sensitivity was measured at test locations between the fovea and 5.0° eccentricity via an increment threshold approach using a 40-trial, yes-no adaptive Bayesian staircase procedure (QUEST). A high-speed eye tracking algorithm enabled real-time video stabilization and the delivery of diffraction-limited Goldmann I-sized stimuli (diameter = 6.5 arc min = ∼32 μm; λ = 680 nm) to targeted retinal loci for 200 ms. Test locations were selected either manually by the examiner or automatically using Fourier-based image registration. Cone spacing was assessed at each test location and sensitivity was plotted against retinal eccentricity. Finally, a 4.2 arc min stimulus was used to probe the angioscotoma associated with a blood vessel located at 2.5° eccentricity. Visual sensitivity decreases with eccentricity at a rate of -1.32 dB/deg (R = 0.60). The vertical and horizontal errors of the targeted stimulus delivery algorithm averaged 0.81 and 0.89 arc min (∼4 μm), respectively. Based on a predetermined exclusion criterion, the stimulus was successfully delivered to its targeted location in 90.1% of all trials. Automated recovery of test locations afforded the repeat testing of the same set of cones over a period of 3 months. Thresholds measured over a parafoveal blood vessel were 1.96 times higher (p < 0.05; one-tailed t-test) than those measured in directly adjacent retina. AOSLO-based microperimetry has the potential to test visual sensitivity with fine retinotopic precision. Automated recovery of previously tested locations allows these measures to be tracked longitudinally. This approach can be implemented by researchers interested in establishing the functional correlates of photoreceptor mosaic structure in patients with retinal disease.

A robust real-time video stabilization algorithm

The acquisition of digital video usually suffers from undesirable camera jitters due to unstable camera motions. In this paper, we propose a robust real-time video stabilization algorithm that alleviates the undesirable jitter motions from the unstable video to produce a stabilized video. In the proposed algorithm, we first compute the sparse optical flow vectors between successive frames, followed by estimating the camera motion by fitting the computed optical flow vectors to a simplified affine motion model with a robust trimmed least squares method. Then the computed camera motion parameters are smoothed temporally to reduce the motion fluctuations by using a regularization method. Finally, we transform all frames in the video sequence based on the original and smoothed camera motions to obtain a stabilized video. Experimental results are given to demonstrate the stabilization performance and the efficiency of the proposed algorithm.