High-Level Codewords Based on Granger Causality for Video Event Detection

Abstract EN

Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis.

In this paper, we propose a new framework to perceive high-level codewords by analyzing temporal relationship between different channels of video features.

The low-level vocabulary words are firstly generated after different audio and visual feature extraction.

A weighted undirected graph is constructed by exploring the Granger Causality between low-level words.

Then, a greedy agglomerative graph-partitioning method is used to discover low-level word groups which have similar temporal pattern.

The high-level codebooks representation is obtained by quantification of low-level words groups.

Finally, multiple kernel learning, combined with our high-level codewords, is used to detect the video event.

Extensive experimental results show that the proposed method achieves preferable results in video event detection.