鲁棒无监督运动模式推断视频和应用

Xuemei Zhao, G. Medioni
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引用次数: 31

摘要

我们提出了一个无监督学习框架来推断视频中的运动模式,并反过来使用它们来改进对静态摄像机序列中运动物体的跟踪。基于轨迹点,我们使用流形学习方法Tensor Voting来推断(x, y)空间中的局部几何结构,并将轨迹点嵌入到(x, y, θ)空间中,其中θ表示运动方向。在这个空间中,点自动形成内在的流形结构,每个流形结构对应一个运动模式。为了定义每个组,提出了一种新的鲁棒流形分组算法。执行张量投票以提供多个几何线索,这些线索在任意对点之间形成多个相似核,并在此多核设置中使用谱聚类技术。在我们的应用中,分组算法比最先进的方法具有更好的性能。然后,提取的运动模式可以用作改进任何目标跟踪器性能的先验。它对减少误报和ID开关特别有用。在具有挑战性的现实世界序列上进行了实验,结果的定量分析表明,该框架有效地改进了最先进的跟踪器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust unsupervised motion pattern inference from video and applications
We propose an unsupervised learning framework to infer motion patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a motion pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted motion patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.
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