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25.07.2018 | Ausgabe 3/2019 Open Access

Neural Processing Letters 3/2019

Video Synchronization Based on Projective-Invariant Descriptor

Neural Processing Letters > Ausgabe 3/2019
Qiang Zhang, Lin Yao, Yajun Li, Jungong Han


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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