Motion-based video parsing methods segment video streams according to changes of camera motion types. They rely usually on compressed video streams, where motion vector fields are provided. Camera parameters can be derived from these motion vectors. There are a number of relevant video codecs where no motion information is included. For such video streams, camera parameters have to be estimated using a frame-to-frame image registration method. In our approach, we provide both techniques to estimate camera parameters. Enhanced feature extraction algorithms take advantage of estimated parameters. For classification, the method uses three multi-class Support Vector Machines (M-SVMs) to independently detect pan, tilt, and zoom camera motion as well as the direction of motion. Experimental results show a promising performance of our generic approach with test video streams from the TRECVid 2005 BBC rushes video corpus.
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机译：基于运动的视频解析方法根据摄像机运动类型的变化对视频流进行分段。它们通常依赖于提供运动矢量场的压缩视频流。摄像机参数可以从这些运动矢量中得出。有许多相关的视频编解码器，其中不包含运动信息。对于此类视频流，必须使用帧到帧图像配准方法估计摄像机参数。在我们的方法中，我们提供了两种估算相机参数的技术。增强的特征提取算法利用了估计的参数。对于分类，该方法使用三个多类支持向量机（M-SVM）来独立检测摇摄，倾斜和变焦相机运动以及运动方向。实验结果表明，我们的通用方法在TRECVid 2005 BBC rush视频语料库的测试视频流中表现出令人鼓舞的性能。
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