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Scene-adaptive video partitioning by semantic object tracking

机译：通过语义对象跟踪的场景自适应视频分区

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摘要

An adaptive mechanism for video partitioning by semantic objects tracking is proposed. A video scene consists of the sequence of frames between two adjacent video scene changes which can be detected according to the video scene complexity. In general, the video scene complexity can be described in twofold characteristics—the temporal domain motion complexity and the spatial domain activity complexity. For this purpose, we propose a novel spatial-temporal segmentation method as a general segmentation algorithm combining several types of information including color and motion. A region within a foreground object is called as a foreground region, which is characterized as a moving uniform region. An algorithm for object tracking based on the foreground regions is also included in order to recognize camera and object movements and obtain correct video shots. By analyzing foreground objects between consecutive frames, the types of scene change and the types of camera movement can be detected according to the number of entering and existing regions and the motion vectors, respectively. Based on these parameters, the frames of a video sequence are categorized into normal, cut, fade, and dissolve classes. Adaptation is realized by grouping variable number of the labeled frames as a unit, which contains a scene change to be automatically determined by the moment-preserving thresholding techniques. Experimental results are presented to demonstrate the performance of the new method in terms of better segmentation.
机译：提出了一种基于语义对象跟踪的视频分割自适应机制。视频场景由两个相邻视频场景变化之间的帧序列组成，可以根据视频场景的复杂性进行检测。通常，视频场景复杂度可以用双重特征来描述-时域运动复杂度和空间域活动复杂度。为此，我们提出了一种新颖的时空分割方法，作为一种结合了包括颜色和运动在内的多种信息的一般分割算法。前景对象内的区域称为前景区域，其特征是移动的均匀区域。还包括一种基于前景区域的对象跟踪算法，以识别相机和对象的运动并获得正确的视频镜头。通过分析连续帧之间的前景对象，可以分别根据进入区域和存在区域的数量以及运动矢量来检测场景变化的类型和摄像机移动的类型。根据这些参数，视频序列的帧可分为正常，剪切，淡入淡出和溶解类别。通过将可变数量的标记帧分组为一个单元来实现自适应，该单元包含一个场景变化，该场景变化将由保留力矩的阈值技术自动确定。实验结果表明，该方法在更好的分割方面表现出了新的性能。

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