SAIVT-Campus Dataset

SAIVT-Campus Dataset

Overview

The SAIVT-Campus Database is an abnormal event detection database captured on a university campus, where the abnormal events are caused by the onset of a storm. Contact  or  for more information.

Licensing

The SAIVT-Campus database is © 2012 QUT and is licensed under the .

Attribution

To attribute this database, please include the following citation:
Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. available at .

Acknowledging the Database in your Publications

In addition to citing our paper, we kindly request that the following text be included in an acknowledgements section at the end of your publications: 
We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-Campus database for our research.

Installing the SAIVT-Campus database

After downloading and unpacking the archive, you should have the following structure: 

SAIVT-Campus
+-- LICENCE.txt
+-- README.txt
+-- test_dataset.avi
+-- training_dataset.avi
+-- Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf

Notes

The SAIVT-Campus dataset is captured at the Queensland University of Technology, Australia.

It contains two video files from real-world surveillance footage without any actors:

training_dataset.avi (the training dataset)
test_dataset.avi (the test dataset).

This dataset contains a mixture of crowd densities and it has been used in the following paper for abnormal event detection:

Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) Activity analysis in complicated scenes using DFT coefficients of particle trajectories. In 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2012), 18-21 September 2012, Beijing, China. Available at . 
This paper is also included with the database (Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf) Both video files are one hour in duration.

The normal activities include pedestrians entering or exiting the building, entering or exiting a lecture theatre (yellow door), and going to the counter at the bottom right. The abnormal events are caused by a heavy rain outside, and include people running in from the rain, people walking towards the door to exit and turning back, wearing raincoats, loitering and standing near the door and overcrowded scenes. The rain happens only in the later part of the test dataset.

As a result, we assume that the training dataset only contains the normal activities. We have manually made an annotation as below:

the training dataset does not have abnormal scenes
the test dataset separates into two parts: only normal activities occur from 00:00:00 to 00:47:16 abnormalities are present from 00:47:17 to 01:00:00. We annotate the time 00:47:17 as the start time for the abnormal events, as from this time on we have begun to observe people stop walking or turn back from walking towards the door to exit, which indicates that the rain outside the building has influenced the activities inside the building. Should you have any questions, please do not hesitate to contact .

SAIVT-Campus 数据集

概述
SAIVT-Campus 数据库是一款采集自大学校园的异常事件检测数据库，其中异常事件由暴雨天气引发。如需了解更多信息，请联系 或 。

授权声明
SAIVT-Campus 数据库版权归2012年昆士兰科技大学（Queensland University of Technology, QUT）所有，并采用 授权协议。

引用规范
引用本数据库时，请包含以下著录信息：Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) 基于粒子轨迹离散傅里叶变换（Discrete Fourier Transform, DFT）系数的复杂场景活动分析. 收录于：第9届IEEE高级视频与信号监控国际会议（AVSS 2012），2012年9月18日至21日，中国北京。可于 处获取。

出版物致谢要求
除引用本论文外，恳请您在出版物末尾的致谢章节中加入以下文字："我们感谢昆士兰科技大学（QUT）SAIVT研究实验室为我们的研究免费提供SAIVT-Campus数据集。"

数据集安装说明
下载并解压 归档文件后，您将获得如下目录结构：
SAIVT-Campus
+-- LICENCE.txt
+-- README.txt
+-- test_dataset.avi
+-- training_dataset.avi
+-- Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf

备注说明
SAIVT-Campus 数据集采集自澳大利亚昆士兰科技大学校园。
该数据集包含两段真实监控录像，无任何演员出镜：
training_dataset.avi（训练数据集）
test_dataset.avi（测试数据集）。

本数据集涵盖多种人群密度场景，并已被用于以下论文的异常事件检测研究：Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2012) 基于粒子轨迹离散傅里叶变换（Discrete Fourier Transform, DFT）系数的复杂场景活动分析. 收录于：第9届IEEE高级视频与信号监控国际会议（AVSS 2012），2012年9月18日至21日，中国北京。可于 处获取。
该论文也随数据集一同提供（文件名为Xu2012 - Activity analysis in complicated scenes using DFT coefficients of particle trajectories.pdf）。两段视频时长均为1小时。

正常活动包括行人进出建筑、进出报告厅（黄色门）以及前往右下角的服务台。异常事件由室外暴雨引发，具体表现为：人们冒雨奔跑、走向出口门后折返、穿着雨衣、在门口附近逗留驻足以及人群拥挤场景。暴雨仅出现在测试集的后半段。

因此，我们假设训练集仅包含正常活动。我们已手动完成如下标注：
训练集无异常场景
测试集分为两个阶段：00:00:00至00:47:16仅存在正常活动，00:47:17至01:00:00则出现异常事件。我们将00:47:17标注为异常事件的起始时间，自该时刻起，可观察到行人停止行进或从走向出口的途中折返，这表明室外降雨已对楼内活动产生影响。如有任何疑问，请随时联系 。