概率区域轨迹的时空聚类

Fabio Galasso, M. Iwasaki, K. Nobori, R. Cipolla
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引用次数: 19

摘要

我们提出了一种新的基于运动的轨迹时空聚类模型,该模型适用于移动摄像机拍摄的行人街景视频序列。我们工作的一个关键贡献是引入了新的概率区域轨迹,其动机是视频序列中帧的分割不可重复。采用最先进的分层分割算法获得分层图像片段,并在有向无环图中从相邻帧连接起来。使用基于动态规划的优化从该图中提取区域轨迹和置信度度量。我们的第二个主要贡献是一个具有双重目标的贝叶斯框架:在最大似然意义上学习基于视频特征的运动模式的随机森林分类器,并从不同帧、长度和层次的区域轨迹构建一个独特的图。最后,我们展示了使用Isomap对行人的区域轨迹进行有效的时空聚类。我们用新的和现有的具有挑战性的视频序列的实验结果支持我们的主张。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatio-temporal clustering of probabilistic region trajectories
We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences.
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