Video stabilization for a hand-held camera based on 3D motion model

Wang, J. M.; Chou, H. P.; Chen, S. W.; Fuh, Chiou-Shann

doi:10.1109/icip.2009.5413831

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…To handle this camera motion, some methods use image stabilization techniques based on motion compensation of feature points to achieve a stable background and then apply background subtraction methods to detect moving objects [96,97]. For the case of handheld cameras, camera motion models (eg., 3D motion models [98] or [99]) can be efficiently used to compensate camera vibrations.…”

Section: Camera Motionmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

207

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This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

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Section: Camera Motionmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

207

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“…We compare our algorithm with Traditional Video Stabilization (TVS) [6]. TVS proposed 2D image stabilization that does not require reconstructing the actual 3D camera motion over a long video sequence.…”

Section: A Experimental Environmentmentioning

confidence: 99%

“…We discuss the parameter settings of each algorithm for experimental results as follows. For TVS we follow the suggestion by [6] to set scale parameter as 0 7˜1 For SVS, Gaussian kernel with variance is set as 1~10. In the following, we compare the execution time of each algorithm and the quality for different algorithms with respect to different factors on the mobile phone.…”

Section: A Experimental Environmentmentioning

confidence: 99%

Video stabilization for stereoscopic 3D on 3D mobile devices

Chu

2014

2014 IEEE International Conference on Multimedia and Expo (ICME)

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Advanced 3D mobile devices attract a lot of attentions for 3D visualization nowadays. Stereoscopic images and video taken from the 3D mobile devices are uncomfortable for 3D viewing experiences due to the limited hardware for stereoscopic 3D stabilization. The existing stereoscopic 3D stabilization methods are computationally inef cient for the 3D mobile devices. In this paper, we point out that the above critical issue deteriorates the 3D viewing experiences on the 3D mobile devices. To improve visual comfort, we propose an ef cient and effective algorithm to stabilize the stereoscopic images and video for the 3D mobile devices. To rectify the video jitter, we use the gyroscope and accelerometer embedded on the mobile devices to obtain the geometry information of the cameras. Different from video-content-based motion estimation, our algorithm based on the gyroscope and acceleration data can achieve higher accuracy to effectively stabilize the video. Therefore, our approach is robust in video stabilization even under poor lighting and substantial foreground motion. Our algorithm outperforms previous approaches in not only smaller running time but also the better comfort of the stereoscopic 3D visualization for the 3D mobile devices.Index Terms-Stereoscopic 3D Image, 3D mobile devices.

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“…According to the motion models being considered, the already proposed global ME techniques for DVS system can roughly be divided into two categories: (1) twodimensional stabilization techniques which deal with translational jitter only [9][10][11][12][13][14][15][16][17][18][19][20] and (2) multi-dimensional stabilization techniques which aim at stabilizing more complicated fluctuations in addition to translation [21][22][23][24][25]. Most of the existing algorithms fall into the first category because the translation is the most commonly encountered motion and the complexity of estimating translation parameters is relatively low for real-time stabilization.…”

Section: Introductionmentioning

confidence: 99%

Digital video stabilizer by adaptive fuzzy filtering

2012

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Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel DVS algorithm that compensates the camera jitters applying an adaptive fuzzy filter on the global motion of video frames. The adaptive fuzzy filter is a simple infinite impulse response filter which is tuned by a fuzzy system adaptively to the camera motion characteristics. The fuzzy system is also tuned during operation according to the amount of camera jitters. The fuzzy system uses two inputs which are quantitative representations of the unwanted and the intentional camera movements. The global motion of video frames is estimated based on the block motion vectors which resulted by video encoder during motion estimation operation. Experimental results indicate a good performance for the proposed algorithm.

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Video stabilization for a hand-held camera based on 3D motion model

Cited by 23 publications

References 12 publications

New trends on moving object detection in video images captured by a moving camera: A survey

New trends on moving object detection in video images captured by a moving camera: A survey

Video stabilization for stereoscopic 3D on 3D mobile devices

Digital video stabilizer by adaptive fuzzy filtering

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