Video saliency detection, which suffers from the interference of complicated motion and complex background, aims at discovering the motion-related and the most noticeable object in a video sequence while maintaining the spatiotemporal consistency of saliency maps. We propose a video saliency detection approach by constructing the moving object properties, meaning that motion is in concurrence with a general object for detected regions. With the assistance of different key frame strategies, the consistency propagation, which is respectively conducted to refine the obtained results of motion saliency and object proposals to improve low-quality detections and avoid the excessive accumulation of errors, is orderly implemented through sparse reconstruction based on the constructed adjacent relationships. Meanwhile, by integrating the refined object proposals with the refined salient motion detection, a pixel-based fusion strategy is applied to locate the most trustworthy motion object regions and suppress detection noises, such as dynamic background and stationary objects. Moreover, a Bayesian fusion framework that incorporates the global features obtained using low-rank constraints is employed to further enhance the accuracy and global temporal consistency for the obtained initial motion object regions. Then based on the obtained priors of motion objects and background regions, the spatial saliency map is estimated using the geodesic distances transform to discover more complete and spatially salient motion objects. Finally, an energy optimization function is proposed to intuitively integrate multiple saliency clues (i.e., spatial saliency, global features, and motion objects) and generate the global spatiotemporal consistency saliency map. Experimental results on three different benchmark datasets demonstrate that the proposed method successfully infers video moving objects and extracts the most salient regions, outperforming the state-of-the-art methods.

中文翻译：

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基于运动对象属性的视频显着性检测

遭受复杂运动和复杂背景干扰的视频显着性检测旨在发现视频序列中与运动相关且最引人注意的对象，同时保持显着性图的时空一致性。我们提出了一种通过构造运动对象属性的视频显着性检测方法，这意味着运动与被检测区域的一般对象一致。借助不同的关键帧策略，分别通过基于稀疏重建有序地实现一致性传播，以分别改进运动显着性和对象建议的结果，以改善低质量检测并避免错误的过度积累。构造的相邻关系。同时，通过将改进的对象建议与改进的显着运动检测相集成，基于像素的融合策略被应用于定位最值得信赖的运动对象区域并抑制检测噪声，例如动态背景和静止对象。此外，采用贝叶斯融合框架，该框架合并了使用低秩约束获得的全局特征，以进一步提高获得的初始运动对象区域的准确性和全局时间一致性。然后，基于获得的运动对象和背景区域的先验，使用测地距离变换估计空间显着性图，以发现更完整且在空间上显着的运动对象。最后，提出了一种能源优化功能，以直观地整合多个显着性线索（即空间显着性，全局特征，和运动对象），并生成全局时空一致性显着图。在三个不同基准数据集上的实验结果表明，该方法成功地推断了视频运动对象并提取了最显着的区域，性能优于最新方法。