Scene Segmentation for Behaviour Correlation
Scene Segmentation for Behaviour Correlation
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DOI：
10.1007/978-3-540-88693-8_28
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Jian Li;S. Gong;T. Xiang
中科院分区：
文献类型：
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作者：
Jian Li;S. Gong;T. Xiang
本文提出了一种新的框架，用于检测异常的行人和车辆的行为，通过建模不同的共同出现的对象之间的局部和全局在一个给定的场景中的互相关。我们解决这个问题，首先分割一个场景到语义区域，根据对象事件如何发生在全球的场景中，第二建模区域对象事件之间的并发相关性本地（同一区域内）和全球（跨不同区域）。该模型不是跟踪对象，而是基于原子视频事件的分类来表示行为，旨在更适合分析拥挤的场景。所提出的系统工作在一个无监督的方式在整个使用自动模型阶数选择，以估计其参数给定的视频数据的场景为一个简短的训练周期。我们证明了该系统的有效性与实验上的公共道路交通数据。
This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according to how object events occur globally in the scene, and second modelling concurrent correlations among regional object events both locally (within the same region) and globally (across different regions). Instead of tracking objects, the model represents behaviour based on classification of atomic video events, designed to be more suitable for analysing crowded scenes. The proposed system works in an unsupervised manner throughout using automatic model order selection to estimate its parameters given video data of a scene for a brief training period. We demonstrate the effectiveness of this system with experiments on public road traffic data.