Gaussian process regression flow for analysis of motion trajectories

Kihwan Kim, Dongryeol Lee, Irfan Essa
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引用次数: 183

Abstract

Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data. Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates.
高斯过程回归流分析运动轨迹
识别视频中物体的运动和活动需要有效的表示来分析和匹配运动轨迹。在本文中,我们引入了一种专门用于匹配运动轨迹的新表示。我们用高斯过程回归从稀疏的向量序列集将轨迹建模为连续的密集流场。此外,我们还引入了一种随机采样策略,用于从有限的数据中学习稳定的运动类别。我们的表示允许增量预测可能的路径,并从在线轨迹检测异常事件。这种表示还支持在轨迹中匹配具有加速度变化和暂停或停止的复杂运动。我们使用该方法对交通监控域中的运动轨迹进行分类和预测,并在多个数据集上进行了测试。我们表明,我们的方法在来自不同帧率的各种视频数据集的各种类型的完整和不完整轨迹上都能很好地工作。
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