Activity based surveillance video content modelling

This paper tackles the problem of surveillance video content modelling. Given a set of surveillance videos, the aims of our work are twofold: firstly a continuous video is segmented according to the activities captured in the video; secondly a model is constructed for the video content, based on which an unseen activity pattern can be recognised and any unusual activities can be detected. To segment a video based on activity, we propose a semantically meaningful video content representation method and two segmentation algorithms, one being offline offering high accuracy in segmentation, and the other being online enabling real-time performance. Our video content representation method is based on automatically detected visual events (i.e. `what is happening in the scene'). This is in contrast to most previous approaches which represent video content at the signal level using image features such as colour, motion and texture. Our segmentation algorithms are based on detecting break...

• Type: Journal
• Year: 2008
• Where: PR
• Authors: Tao Xiang, Shaogang Gong
• Keywords: PR 2008, Surveillance Video, Unseen Activity Patterns, Video Content