Video synchronization and its application to object transfer

Cao, Xiaochun; Wu, Lin; Xiao, Jiangjian; Foroosh, Hassan; Zhu, Jigui; Li, Xiaohong

doi:10.1016/j.imavis.2009.04.015

Cited by 14 publications

(14 citation statements)

References 30 publications

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“…More recent works [3], [7], [9], [15], [18], [19] let it be of free form. Clearly, the first case is simpler since only one or two parameters have to be estimated, in contrast to a nonparametric curve of unknown shape.…”

Section: Previous Workmentioning

confidence: 99%

“…Most of these methods rely on the existence of an unknown geometric relationship between the coordinate systems of corresponding frames; these include an affine transform [23], a plane-induced homography [4], [5], [19], [25], the fundamental matrix [3], [14], [22], [25], the trifocal tensor [11], or a deficient rank matrix made of the coordinates of point trajectories tracked along the whole sequence [15], [16], [21], [26]. This assumption makes it possible either to formulate some minimization over the time correspondence parameters (e.g., , ) or to perform an exhaustive search in the range of allowed values.…”

Section: Previous Workmentioning

confidence: 99%

“…The cases in which this geometric relationship is constant [4], [15], [16], [23]- [27], for instance because the two cameras are rigidly attached to each other, are easier to solve. Instead, works [3], [5], [7], [9], [11], [14], [18], [19], [21], [22] address, as we do, the more difficult case of independently moving cameras, where no geometric relationship is assumed beyond a more or less overlapping field of view.…”

Section: Previous Workmentioning

confidence: 99%

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Video Alignment for Change Detection

Diego

Ponsa

Serrat

et al. 2011

IEEE Trans. on Image Process.

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Abstract-In this work, we address the problem of aligning two video sequences. Such alignment refers to synchronization, i.e., the establishment of temporal correspondence between frames of the first and second video, followed by spatial registration of all the temporally corresponding frames. Video synchronization and alignment have been attempted before, but most often in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, restrictive assumptions have been applied, including linear time correspondence or the knowledge of the complete trajectories of corresponding scene points; to some extent, these assumptions limit the practical applicability of any solutions developed. We intend to solve the more general problem of aligning video sequences recorded by independently moving cameras that follow similar trajectories, based only on the fusion of image intensity and GPS information. The novelty of our approach is to pose the synchronization as a MAP inference problem on a Bayesian network including the observations from these two sensor types, which have been proved complementary. Alignment results are presented in the context of videos recorded from vehicles driving along the same track at different times, for different road types. In addition, we explore two applications of the proposed video alignment method, both based on change detection between aligned videos. One is the detection of vehicles, which could be of use in ADAS. The other is online difference spotting videos of surveillance rounds.

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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Video Alignment for Change Detection

Diego

Ponsa

Serrat

et al. 2011

IEEE Trans. on Image Process.

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“…We address this problem by exploiting a novel projective-invariant descriptor based on the cross ratio to obtain the matched trajectory points between the two input videos. So far, numerous video synchronization methods have been presented in the previous works, which are mainly classified into two categories: intensity-based ones [9][10][11][12][13] and feature-based ones [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The intensity-based methods usually rely on colors, intensities, or intensity gradients to achieve the temporal synchronization of overlapping videos.…”

Section: Introductionmentioning

confidence: 99%

“…Among the feature-based video synchronization methods, the trajectory-based ones are one of the most popular categories [19][20][21][22][23][24][25][26][27][28][29]. These methods generally use some epipolar geometry or homography information among different viewpoints for the purpose of exploiting the matched trajectory points or time pairs between (or among) input videos [21,22,26,28].…”

Section: Introductionmentioning

confidence: 99%

Video Synchronization Based on Projective-Invariant Descriptor

Zhang

Yang

et al. 2018

Neural Process Lett

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In this paper, we present a novel trajectory-based method to synchronize two videos shooting the same dynamic scene, which are recorded by stationary un-calibrated cameras from different viewpoints. The core algorithm is carried out in two steps: projective-invariant descriptor construction and trajectory points matching. In the first step, a new five-coplanar-points structure is proposed to compute the cross ratio during the construction of the projective-invariant descriptor. The five points include one trajectory point and four fixed points induced from the background scene, which are co-planar in the 3D coordinate. In the second step, the matched trajectory points are initially estimated by the primitive nearest neighbor method, and are further refined by using epipolar geometric constraints and post processing. Experimental results demonstrate that the proposed method significantly outperforms the existing state-of-the-arts. More importantly, the proposed method is more generic in the sense that it works well for those videos captured under different conditions, including different frame rates, wide baseline, multiple moving objects, planar or non-planar motion trajectories.

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