Video stabilization by estimation of similarity transformation from integral projections

Veldandi, Muninder; Ukil, Soumik; Rao, Krishna Govinda

doi:10.1109/icip.2013.6738162

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Standard video stabilization methods use projective transformation [47], affine transformation [32] or similarity transformation [48], or combinations of them [49,50] as their 2D motion model. However, our initial tests with projective transformation did not give accurate results.…”

Section: Discussionmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.

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Section: Discussionmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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“…A scene with a homogeneous background with small range of motion is depicted in Figure 1, while a scene obtained from an on-road shooting with forward moving platform is depicted in Figure 2. These figures show the results of video stabilization using three methods, such as Derivative Dynamic Time (DDT) warping based on angular integral projections (Veldandi et al, 2013), Fourier Radon (FRadon) warping (Mohamadabadi et al, 2012), and Differential-Radon (DRadon) curve warping (Shukla et al, 2017). As it can be seen, all methods produce the cropped frames that caused a necessity of video completion.…”

Section: Construction Of Pseudo-panoramic Key Framementioning

confidence: 99%

Video Completion in Digital Stabilization Task Using Pseudo-Panoramic Technique

Favorskaya

Buryachenko

Зотин

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Video completion is a necessary stage after stabilization of a non-stationary video sequence, if it is desirable to make the resolution of the stabilized frames equalled the resolution of the original frames. Usually the cropped stabilized frames lose 10-20% of area that means the worse visibility of the reconstructed scenes. The extension of a view of field may appear due to the pan-tilt-zoom unwanted camera movement. Our approach deals with a preparing of pseudo-panoramic key frame during a stabilization stage as a pre-processing step for the following inpainting. It is based on a multi-layered representation of each frame including the background and objects, moving differently. The proposed algorithm involves four steps, such as the background completion, local motion inpainting, local warping, and seamless blending. Our experiments show that a necessity of a seamless stitching occurs often than a local warping step. Therefore, a seamless blending was investigated in details including four main categories, such as featheringbased, pyramid-based, gradient-based, and optimal seam-based blending.

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A new composite multi-constrained differential-radon warping approach for digital video affine motion stabilization

Shukla

Ojha

Jha

2017

Computer Vision and Image Understanding

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Video stabilization by estimation of similarity transformation from integral projections

Cited by 8 publications

References 11 publications

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Video Completion in Digital Stabilization Task Using Pseudo-Panoramic Technique

A new composite multi-constrained differential-radon warping approach for digital video affine motion stabilization

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