Video Sensor-Based Complex Scene Analysis with Granger Causality

Sensors

Yawen Fan¹  Hua Yang¹  Shibao Zheng¹  Hang Su¹ 

[1] Institution of Image Communication and Information Processing, Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China;E-Mails:

关键词: video surveillance;    scene analysis;    topic model;    point process;    Granger causality;   

DOI  :  10.3390/s131013685

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.