Institution of Image Communication and Information Processing, Department of ElectronicEngineering, Shanghai Jiaotong University, Shanghai 200240, China.
Sensors (Basel). 2013 Oct 11;13(10):13685-707. doi: 10.3390/s131013685.
In this report, we propose a novel framework to explore the activity interactions and temporal dependencies between activities in complex video surveillance scenes. Under our framework, a low-level codebook is generated by an adaptive quantization with respect to the activeness criterion. The Hierarchical Dirichlet Processes (HDP) model is then applied to automatically cluster low-level features into atomic activities. Afterwards, the dynamic behaviors of the activities are represented as a multivariate point-process. The pair-wise relationships between activities are explicitly captured by the non-parametric Granger causality analysis, from which the activity interactions and temporal dependencies are discovered. Then, each video clip is labeled by one of the activity interactions. The results of the real-world traffic datasets show that the proposed method can achieve a high quality classification performance. Compared with traditional K-means clustering, a maximum improvement of 19.19% is achieved by using the proposed causal grouping method.
在本报告中，我们提出了一种新的框架来探索复杂视频监控场景中活动之间的交互和时间依赖性。在我们的框架下，通过自适应量化基于活动度准则生成一个低层次代码本。然后应用层次狄利克雷过程（HDP）模型将低层次特征自动聚类成原子活动。之后，活动的动态行为表示为多元点过程。通过非参数格兰杰因果分析显式捕获活动之间的两两关系，从而发现活动之间的交互和时间依赖性。然后，通过其中一种活动交互为每个视频片段进行标注。真实世界交通数据集的结果表明，所提出的方法可以实现高质量的分类性能。与传统的 K-means 聚类相比，使用所提出的因果分组方法可以最大提高 19.19%。
