Digital video stabilization based on adaptive camera trajectory smoothing

Souza, Marcos Roberto e; Pedrini, Hélio

doi:10.1186/s13640-018-0277-7

Cited by 19 publications

(8 citation statements)

References 48 publications

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“…Image stabilization is a fundamental topic in the research field of Computer Vision [35] with many applications in object recognition [36], motion analysis [37], and image restoration [38], among others [39]. Similar stabilization methods have been developed with other purposes outside the field of coastal imagery [40][41][42][43]. One primary problem with existing stabilization methods is that they are sensitive to changes in illumination, making them prone to failure [44].…”

Section: Introductionmentioning

confidence: 99%

A Simple and Efficient Image Stabilization Method for Coastal Monitoring Video Systems

et al. 2019

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Fixed video camera systems are consistently prone to importune motions over time due to either thermal effects or mechanical factors. Even subtle displacements are mostly overlooked or ignored, although they can lead to large geo-rectification errors. This paper describes a simple and efficient method to stabilize an either continuous or sub-sampled image sequence based on feature matching and sub-pixel cross-correlation techniques. The method requires the presence and identification of different land-sub-image regions containing static recognizable features, such as corners or salient points, referred to as keypoints. A Canny edge detector ( C E D ) is used to locate and extract the boundaries of the features. Keypoints are matched against themselves after computing their two-dimensional displacement with respect to a reference frame. Pairs of keypoints are subsequently used as control points to fit a geometric transformation in order to align the whole frame with the reference image. The stabilization method is applied to five years of daily images collected from a three-camera permanent video system located at Anglet Beach in southwestern France. Azimuth, tilt, and roll deviations are computed for each camera. The three cameras showed motions on a wide range of time scales, with a prominent annual signal in azimuth and tilt deviation. Camera movement amplitude reached up to 10 pixels in azimuth, 30 pixels in tilt, and 0.4° in roll, together with a quasi-steady counter-clockwise trend over the five-year time series. Moreover, camera viewing angle deviations were found to induce large rectification errors of up to 400 m at a distance of 2.5 km from the camera. The mean shoreline apparent position was also affected by an approximately 10–20 m bias during the 2013/2014 outstanding winter period. The stabilization semi-automatic method successfully corrects camera geometry for fixed video monitoring systems and is able to process at least 90% of the frames without user assistance. The use of the C E D greatly improves the performance of the cross-correlation algorithm by making it more robust against contrast and brightness variations between frames. The method appears as a promising tool for other coastal imaging applications such as removal of undesired high-frequency movements of cameras equipped in unmanned aerial vehicles (UAVs).

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Section: Introductionmentioning

confidence: 99%

A Simple and Efficient Image Stabilization Method for Coastal Monitoring Video Systems

et al. 2019

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“…• The proposed optimization technique to calculate a new estimate of the corrected motion was published in the IET Image Processing journal (Qualis B1) [40]. • The adaptive Gaussian filter was published in the EURASIP Journal on Image and Video Processing (Qualis B1) [41]. • The paper related to the motion energy image was published in The Visual Computer journal (Qualis A2) [42].…”

Section: Conclusion Publications and Distinctionsmentioning

confidence: 99%

Digital Video Stabilization: Algorithms and Evaluation

Souza

Pedrini

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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Several devices have allowed the acquisition and editing of videos in various circumstances, such as digital cameras, smartphones and other mobile devices. However, the use of cameras under adverse conditions usually results in non-precise motion and occurrence of shaking, which may compromise the stability of the obtained videos. To overcome such problem, digital stabilization aims to correct camera motion oscillations that occur in the acquisition process, particularly when the cameras are mobile and handled in adverse conditions, through software techniques - without the use of specific hardware - to enhance visual quality either with the intention of enhancing human perception or improving final applications, such as detection and tracking of objects. This is important in order to avoid hardware cost and indispensable for videos already recorded. This work proposed three methods to perform digital video stabilization and two other techniques to evaluate video stabilization quality.

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“…To quantify the stabilized videos Peak Signal to Noise Ratio (PSNR) and Interframe Transformation Fidelity (ITF) were used by Walha, Zhou, Marcenaro, and others, [ 27 , 28 , 29 , 30 , 31 , 32 ], just to name a few. PSNR and ITF are image quality measurements based on Mean Squared Error (MSE), which is a pixel-by-pixel comparison of two images and does not take into account any changes in luminance and contrast, which are expected to vary due to heating and cooling of the specimen during an active thermography inspection.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application

Pant

Nooralishahi

Avdelidis

et al. 2021

Sensors

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Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

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Digital video stabilization based on adaptive camera trajectory smoothing

Cited by 19 publications

References 48 publications

A Simple and Efficient Image Stabilization Method for Coastal Monitoring Video Systems

A Simple and Efficient Image Stabilization Method for Coastal Monitoring Video Systems

Digital Video Stabilization: Algorithms and Evaluation

Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application

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