Real-time video stabilization for fast-moving vehicle cameras

Hu, Wu-Chih; Chen, Chao-Ho; Chen, Tsong-Yi; Peng, Min-Yang; Su, Yi-Jen

doi:10.1007/s11042-016-4291-4

Cited by 12 publications

(13 citation statements)

References 17 publications

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“…7, are used for comparison with the state‐of‐the‐art image stabilisation methods. The interframe transformation fidelity (ITF) metric [38, 43] is used as an objective function, which is an average peak signal‐to‐noise ratio (PSNR) and provides an estimate of the stabilisation achieved. The ITF is given by (6):

ITF = \frac{1}{N_{frame} - 1} \sum_{i = 1}^{N_{frame - 1}} PSNR (i)

where

N_{frame}

is the total number of frames in a video sequence.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Such stable frames can be skipped to save considerable time and computation. In [43], the authors used the fact that when a frame contains any jittering effects, the associated optical flow vectors bias towards the direction of camera jitter. This fact forms the basis of the frame‐shaking judgement [43].…”

Section: Proposed Scheme Of Online Image Stabilisationmentioning

confidence: 99%

“…In particular, the points that lie close to each other are clustered using DBSCAN. Generally, the cluster with the maximum number of motion vectors is selected as a primary representation of background motion [38, 43, 44]. This approach can often mislead the system when the number of foreground motion vectors is greater than that of the background, for instance, when a large moving object is present in a scene.…”

Section: Proposed Scheme Of Online Image Stabilisationmentioning

confidence: 99%

“…However, at high speed, incorrect feature points are tracked leading to twisted frames. Relevant work is proposed by Hu et al [43] for video sequences recorded from the moving vehicles. Feature points are classified in background and foreground regions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Yousaf

Hanif

Khan

et al. 2019

IET Computer Vision

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Image stabilisation aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non-static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles, and handheld devices. Many satisfactory solutions to the image stabilisation problem are proposed in the recent literature, but most of these methods are not adaptable for handling a wide range of intentional motions with minimum lag, especially in real-time scenarios. In this study, the authors propose an online two-dimensional image stabilisation technique based on dual motion vector integration, which is a novel adaptive motion smoothing technique that employs an average length of motion vectors to estimate the intentional motion. The overall computational cost of the proposed system is significantly reduced by employing frame-shaking judgment that only allows processing of jittering frames. Promising experimental results have been obtained on challenging videos obtained from hand-held and vehicle-mounted cameras which demonstrate the robustness and effectiveness of the proposed technique against feature point mismatching and presence of moving objects within the scene at a frame rate of 30 frames per second.

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ITF = \frac{1}{N_{frame} - 1} \sum_{i = 1}^{N_{frame - 1}} PSNR (i)

where

N_{frame}

is the total number of frames in a video sequence.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Proposed Scheme Of Online Image Stabilisationmentioning

confidence: 99%

Section: Proposed Scheme Of Online Image Stabilisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Yousaf

Hanif

Khan

et al. 2019

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…Lim et al [24] proposed an algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs), where they designed a suitable model for the global motion of UAV and employed the optical flow tracking. Hu et al [25] proposed a real-time video stabilization system for the video sequences captured by a fast-moving in-vehicle camera. The proposed method used feature points to evaluate the global motion and the feature points are checked based on LK (Lucas-Kanade) optical flow method.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

et al. 2020

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The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.

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Video Stabilization of the NAO Robot Using IMU Data

Alquisiris-Quecha

Martínez-Carranza

2019

Studies in Computational Intelligence

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Real-time video stabilization for fast-moving vehicle cameras

Cited by 12 publications

References 17 publications

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

Video Stabilization of the NAO Robot Using IMU Data

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