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Research on Video Saliency Detection Via Contrast and Self-Adaptive Transfer

机译:对比度和自适应传输的视频显着性检测研究

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Although a lot of studies in salient motion detection have achieved great success in recent years, many challenges still exist toward the video saliency detection over the non-stationary videos and videos with slowly-moving objects, which supposes to exhibit significant influence on its corresponding subsequent applications. Thus, it urgently needs a more robust, stable, and precise method to solve the above mentioned limitations. In fact, inspired from the basic visualization rule of the human vision system, the human's attention can be easily attracted by two independent factors: the motion saliency clue and the color saliency clue. Hence, this paper develops a novel salient motion detection method by fusing the motion saliency with the color saliency, which refines the preliminary saliency map by self-adaptive transfer via the newly designed intra-frame correlation. Also, comprehensive experimental results of our method toward the state-of-the-art methods over 4 public available benchmarks demonstrate the superiority of our method both in its robustness and high detection precision.
机译:尽管近年来在显着运动检测方面的许多研究取得了巨大的成功,但对于非平稳视频和具有缓慢移动的对象的视频,视频显着性检测仍然存在许多挑战,这对其后继的视频显示出显着的影响。应用程序。因此,迫切需要更鲁棒,稳定和精确的方法来解决上述限制。实际上,受人类视觉系统基本可视化规则的启发,可以很容易地通过两个独立的因素吸引人的注意力:运动显着性线索和颜色显着性线索。因此,本文通过将运动显着性与颜色显着性相融合,开发了一种新颖的显着运动检测方法,该方法通过新设计的帧内相关性,通过自适应传递来完善初始显着性图。此外,我们的方法在4个公开基准上针对最先进方法的综合实验结果表明,该方法在鲁棒性和高检测精度方面均具有优势。

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