Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

Jeon, Semi; Yoon, Inhye; Jang, Jinbeum; Yang, Shijian; Kim, Jisung; Paik, Joonki

doi:10.3390/s17020337

Cited by 20 publications

(12 citation statements)

References 33 publications

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“…This is because the internal sensors cannot detect any apparent motion in the captured images. DIS systems can stabilize input images by compensating the residual fluctuation motion using an image processing technique that estimates the local motion vectors such as block matching [ 45 , 46 , 47 ], bit-plane matching [ 48 , 49 ], feature point matching [ 50 , 51 , 52 , 53 , 54 , 55 ] and optical flow estimation [ 56 , 57 , 58 , 59 ]. Most of these DIS systems do not need any additional mechanical or optical device, and this feature makes them suitable for low-cost electronics.…”

Section: Related Workmentioning

confidence: 99%

Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror

Inoue

Jiang

et al. 2017

Sensors

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This study proposes a novel concept of actuator-driven frame-by-frame intermittent tracking for motion-blur-free video shooting of fast-moving objects. The camera frame and shutter timings are controlled for motion blur reduction in synchronization with a free-vibration-type actuator vibrating with a large amplitude at hundreds of hertz so that motion blur can be significantly reduced in free-viewpoint high-frame-rate video shooting for fast-moving objects by deriving the maximum performance of the actuator. We develop a prototype of a motion-blur-free video shooting system by implementing our frame-by-frame intermittent tracking algorithm on a high-speed video camera system with a resonant mirror vibrating at 750 Hz. It can capture 1024 × 1024 images of fast-moving objects at 750 fps with an exposure time of 0.33 ms without motion blur. Several experimental results for fast-moving objects verify that our proposed method can reduce image degradation from motion blur without decreasing the camera exposure time.

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Section: Related Workmentioning

confidence: 99%

Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror

Inoue

Jiang

et al. 2017

Sensors

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“…Although the speed has been improved, it can only eliminate the movement of a single object. Jeon et al [ 9 ] used particles to update the key points, but this method only has good performances for fixed cameras. Wu et al [ 10 ] used K-means clustering to filter the background feature points, but it can only be applied when the background block is larger than the foreground block.…”

Section: Introductionmentioning

confidence: 99%

Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Zhang

et al. 2021

Sensors

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Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.

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“…For frameby-frame image stabilization in video sequence, the DIS systems can produce a compensated video sequence. The residual fluctuated motion in images can be reduced using various image processing techniques to estimate the local motion vectors, such as block matching [20][21][22][23], bit-plane matching [24,25], Kalman-filter-based prediction [26][27][28][29][30], DFT filtering [31], particle filter [32], scale-invariant feature [33,34], feature point matching [35][36][37][38][39], and optical flow estimation [40][41][42][43][44][45]. These systems do not require any additional mechanism or optical device for video stabilization, and they have been used as low-cost video stabilizers in various applications such as airborne shooting [46][47][48][49][50][51][52], off-road vehicles [53], and teleoperated applications [54][55][56][57], including commercial applications [58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Real-time high-resolution video stabilization using high-frame-rate jitter sensing

et al. 2019

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In this study, the novel approach of real-time video stabilization system using a high-frame-rate (HFR) jitter sensing device is demonstrated to realize the computationally efficient technique of digital video stabilization for high-resolution image sequences. This system consists of a high-speed camera to extract and track feature points in graylevel 512 × 496 image sequences at 1000 fps and a high-resolution CMOS camera to capture 2048 × 2048 image sequences considering their hybridization to achieve real-time stabilization. The high-speed camera functions as a real-time HFR jitter sensing device to measure an apparent jitter movement of the system by considering two ways of computational acceleration; (1) feature point extraction with a parallel processing circuit module of the Harris corner detection and (2) corresponding hundreds of feature points at the current frame to those in the neighbor ranges at the previous frame on the assumption of small frame-to-frame displacement in high-speed vision. The proposed hybrid-camera system can digitally stabilize the 2048 × 2048 images captured with the high-resolution CMOS camera by compensating the sensed jitter-displacement in real time for displaying to human eyes on a computer display. The experiments were conducted to demonstrate the effectiveness of hybrid-camera-based digital video stabilization such as (a) verification when the hybrid-camera system in the pan direction in front of a checkered pattern, (b) stabilization in video shooting a photographic pattern when the system moved with a mixed-displacement motion of jitter and constant low-velocity in the pan direction, and (c) stabilization in video shooting a real-world outdoor scene when an operator holding hand-held hybrid-camera module while walking on the stairs.

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Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

Cited by 20 publications

References 33 publications

Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror

Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror

Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Real-time high-resolution video stabilization using high-frame-rate jitter sensing

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