Unsupervised Discovery of Activities and their Temporal Behaviour

被引：2

作者：

Faruquie, Tanveer A. ^{[1
]}

Banerjee, Subhashis ^{[1
]}

Kalra, Prem K. ^{[1
]}

机构：

[1] Indian Inst Technol Delhi, Dept Comp Sci & Engn, Delhi, India

来源：

2012 IEEE NINTH INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL-BASED SURVEILLANCE (AVSS) | 2012年

关键词：

D O I：

10.1109/AVSS.2012.79

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper addresses the problem of discovering activities and their temporal significance in surveillance videos in an unsupervised manner. We propose a generative model that can jointly capture the activities and their behaviour over time. We use multinomial distribution over local motion features to model activities and a mixture distribution over their time stamps to capture the multi-modal temporal distribution of these activities. We give a Gibbs sampling algorithm to infer the parameters of the model. We demonstrate the effectiveness of our approach on real life surveillance feed of outdoor scenes.

引用

页码：100 / 105

页数：6