Image stabilization using FIR filters

Park, Richard Y.; Pak, Jung Min; Ahn, Choon Ki; Lim, Myo Taeg

doi:10.1109/iccas.2015.7364819

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Kalman filter was used for estimating the smooth camera trajectory. Richard et al [14] have designed an FIR filter for the image stabilization but have demonstrated large missing image areas. The performance is compared with the Kalman filter for stabilization.…”

Section: Literature Reviewmentioning

confidence: 99%

Global Video Stabilization with Truncated Motion Estimation Model and Motion Smoothening

Makwana¹,

Parihar²

2020

IJEIT

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Extensive use of low-cost handheld cameras triggers handshakes and camera gestures to the majority of captured images. Methods to eliminate undesired camera movements from Video sequences have been developed for Video Stabilization (VS). Movement of camera-related artifacts may also affect the efficiency of most 2D video stabilization processes. By improving the motion estimation or motion fluidity process, the performance of the VS procedure can be improved. This article therefore proposes a global approach for video stabilization, which combines an efficient motion smoothing algorithm with a truncating motion calculation. Smoothing up of the high-frequency movements within frames achieves the stabilization. During the first step, the global movement estimate is selecting global motion vectors with the truncated Taylor series expansion. An FIR philter is used in this article to smooth global movement vectors by estimated motion vectors to stabilize shaky images. The proposed method eliminates undesirable movements efficiently and also conserves video information. Motion smoothening eliminates the loss of frame areas after stabilization. The method's efficiency is also compared with the latest global video stability results.

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Section: Literature Reviewmentioning

confidence: 99%

Global Video Stabilization with Truncated Motion Estimation Model and Motion Smoothening

Makwana¹,

Parihar²

2020

IJEIT

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“…Researchers have done a lot of work on these problems. Park et al [20] proposed a new image stabilization method based on finite impulse response filter, which is more robust against mistuning on the model parameters than the KF. Choi et al [21] used extend Kalman filter in aerial airborne imaging to remove the jitter of the camera and retain scanning motion.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Zhang

Yang

et al. 2022

Chin. J. Mech. Eng.

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Noise, vibration and harshness (NVH) problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles. Although high accuracy manufacturing, modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts; off-road condition, obstacles and extreme operating condition could still trigger NVH problems unexpectedly. This paper proposes a vehicular electronic image stabilization (EIS) system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles. Firstly, feature point detection and matching based on an oriented FAST and rotated BRIEF (ORB) algorithm are implemented to match images in the process of EIS. Furthermore, a novel improved random sampling consensus algorithm (i-RANSAC) is proposed to eliminate mismatched feature points and increase the matching accuracy significantly. And an adaptive Kalman filter (AKF) is applied to improve the adaptability of the vehicular EIS. Finally, an experimental platform based on a gasoline model car was established to validate its performance. The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

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“…Researchers have done a lot of work on this problem. Park [16] proposes a new image stabilization method based on finite impulse response filter, which is more robust against mistuning on the model parameters than the KF. Choi [17] used Extend Kalman Filter in aerial airborne imaging to remove the jitter of the camera and retain scanning motion.…”

Section: Introductionmentioning

confidence: 99%

A Vehicular Electronic Image Stabilization System Based on A Gasoline Model Car Platform

Zhang¹,

Yang²,

Wu³

et al. 2020

Preprint

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The image sequence captured by the vehicular camera vibrates due to the vehicle vibration or harsh road conditions. It greatly affects the subsequent image processing. As a video processing technology, electronic image stabilization (EIS) can solve this problem effectively. In this paper, a gasoline model car platform is established, which has rich vibration characteristics. Besides, high unevenness roads are selected to make the vibration problem more prominent. Considering vehicle demand, feature point detection and matching algorithm is applied to match images in the process of establishing EIS. In addition, the algorithm of eliminating mismatched feature points and the adaptive Kalman filter (AKF) is improved to make them more suitable for vehicle application. The experimental results show that the EIS developed in this paper meets the real-time requirements and can work normally in harsh road conditions. Since the platform and working scenarios are more extreme than the normal conditions, the EIS developed in this paper has high portability in the environment perception system of smart car.

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Image stabilization using FIR filters

Cited by 4 publications

References 7 publications

Global Video Stabilization with Truncated Motion Estimation Model and Motion Smoothening

Global Video Stabilization with Truncated Motion Estimation Model and Motion Smoothening

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

A Vehicular Electronic Image Stabilization System Based on A Gasoline Model Car Platform

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