Employing Topical Relations in Semantic Analysis of Traffic Videos

Abstract

Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos.