# A Dataset for Automatic Violence Detection in Videos
This repository contains 350 video clips labelled as “non-violent” and “violent”, to be used to train and test algorithms for violence detection in videos. The non-violent clips are specifically recorded to include behaviours (hugs, claps, exulting, etc.) that can cause false positives in the violence detection task, due to fast movements and the similarity with violent behaviours.
## Data Description
The dataset is split into two main directories, “non-violent” and “violent”, labelling the included clips as showing non-violent behaviours and violent behaviours respectively.
violence-detection-dataset
├─ non-violent
│ ├─ cam1 (60 .mp4 clips)
│ └─ cam2 (60 .mp4 clips)
└─ violent
├─ cam1 (115 .mp4 clips)
└─ cam2 (115 .mp4 clips)
The directories are split into two subdirectories, “cam1” and “cam2”:
- “non-violent/cam1” includes 60 clips representing non-violent behaviours;
- “non-violent/cam2” includes 60 clips with the same non-violent behaviours in “non-violent/cam1” but recorded with a different camera and from a different point of view;
- “violent/cam1” includes 115 clips representing violent behaviours;
- “violent/cam2” includes 115 clips with the same violent behaviours in “violent/cam1” but recorded with a different camera and from a different point of view.
An additional labelling is provided in three csv files available in the main data repository directory (violence-detection-dataset):
- “action-class-occurrences.csv” lists all the actions recorded in the clips, with the number of times each action occurs in the dataset and a label to explain if the action is violent (y) or not (n);
- “non-violent-action-class.csv” lists the actions included in each non-violent clip;
- “violent-action-class.csv” lists the actions included in each violent clip.
The 350 clips in the dataset are MP4 video files (H.264 codec) of an average length of 5.63 seconds, with the shortest video lasting 2 seconds and the longest 14 seconds. For all the clips, the resolution is 1920 x 1080 pixels and the frame rate 30 fps.
The clips were recorded with two cameras placed in two different spots, building a dataset with videos from two different points of view. The cameras are:
- The front camera of the Asus Zenfone Selfie ZD551KL (13 MP, Auto Focus, f/2.2).
- The TOPOP Action Cam OD009B (12 MP, fisheye lens 170°).
All the clips were recorded in the same room, with natural lighting conditions. The Asus Zenfone was placed in the top left corner in front of the door, while the Action Cam was placed in the top right corner on the door side. All the performed actions and behaviours were recorded with both cameras. Therefore, all the clips with the same label and name, but in different final directories (for example “non-violent/cam1/1.mp4” and “non-violent/cam2/1.mp4”) represent the same action, recorded from two different perspectives (the “cam1” directory identifies the Asus Zenfone, while the “cam2” directory identifies the Action Cam).
The clips were performed by a group of non-professional actors, varying from 2 to 4 per clip. For the violent clips, the actors were asked to simulate actions frequent in brawls, such as kicks, punches, slapping, clubbing (beating with a cane), stabbing, and gunshots. For the non-violent clips, the actors were asked to simulate actions which can result in false positives by violence detection techniques due to the speed of movements or the similarity with violent actions. Specifically, the non-violent clips include actions such as hugging, high fives and clapping, exulting, and gesticulating.
## Dataset Release Agreement
The dataset is freely released for research and educational purposes. Please cite as
- M. Bianculli, N. Falcionelli, P. Sernani, S. Tomassini, P. Contardo, M. Lombardi, A.F. Dragoni, A dataset for automatic violence detection in videos, Data in Brief 33 (2020). doi:10.1016/j.dib.2020.106587.
Bibtex entry:
@article{Bianculli2020,
title = "A dataset for automatic violence detection in videos",
journal = "Data in Brief",
volume = "33",
pages = "106587",
year = "2020",
doi = "10.1016/j.dib.2020.106587",
author = "Miriana Bianculli and Nicola Falcionelli and Paolo Sernani and Selene Tomassini and Paolo Contardo and Mara Lombardi and Aldo Franco Dragoni",
}
The paper is open access and available here <https://www.sciencedirect.com/science/article/pii/S2352340920314682>.
用于视频中自动暴力检测的数据集 （355个子文件） 
violent-action-classes.csv 2KB nonviolent-action-classes.csv 1023B action-class-occurrences.csv 213B .gitignore 76B readme.md 5KB
15.mp4 7.78MB 37.mp4 7.51MB 86.mp4 7.08MB 86.mp4 6.96MB 15.mp4 6.94MB 37.mp4 6.52MB 43.mp4 6.23MB 15.mp4 6.16MB 67.mp4 5.96MB 87.mp4 5.93MB 54.mp4 5.86MB 43.mp4 5.71MB 54.mp4 5.67MB 38.mp4 5.56MB 13.mp4 5.51MB 98.mp4 5.26MB 67.mp4 5.21MB 38.mp4 5.11MB 15.mp4 5.1MB 76.mp4 5.07MB 87.mp4 4.98MB 98.mp4 4.89MB 55.mp4 4.71MB 31.mp4 4.59MB 8.mp4 4.58MB 50.mp4 4.45MB 104.mp4 4.44MB 88.mp4 4.44MB 75.mp4 4.37MB 82.mp4 4.37MB 23.mp4 4.33MB 49.mp4 4.33MB 13.mp4 4.32MB 104.mp4 4.27MB 35.mp4 4.25MB 56.mp4 4.22MB 55.mp4 4.21MB 51.mp4 4.2MB 33.mp4 4.18MB 49.mp4 4.18MB 18.mp4 4.13MB 42.mp4 4.12MB 45.mp4 4.09MB 88.mp4 4.08MB 10.mp4 4.07MB 76.mp4 4.07MB 57.mp4 4.07MB 41.mp4 4.05MB 82.mp4 4.01MB 59.mp4 3.99MB 69.mp4 3.94MB 23.mp4 3.94MB 42.mp4 3.93MB 26.mp4 3.93MB 89.mp4 3.89MB 35.mp4 3.88MB 83.mp4 3.88MB 75.mp4 3.85MB 13.mp4 3.83MB 111.mp4 3.82MB 54.mp4 3.81MB 30.mp4 3.8MB 111.mp4 3.78MB 36.mp4 3.77MB 35.mp4 3.74MB 71.mp4 3.74MB 68.mp4 3.74MB 30.mp4 3.74MB 101.mp4 3.71MB 59.mp4 3.69MB 45.mp4 3.69MB 26.mp4 3.69MB 33.mp4 3.69MB 57.mp4 3.66MB 113.mp4 3.65MB 60.mp4 3.64MB 16.mp4 3.63MB 16.mp4 3.63MB 9.mp4 3.62MB 69.mp4 3.6MB 30.mp4 3.59MB 59.mp4 3.56MB 10.mp4 3.56MB 17.mp4 3.55MB 31.mp4 3.55MB 8.mp4 3.54MB 50.mp4 3.54MB 68.mp4 3.52MB 26.mp4 3.51MB 56.mp4 3.5MB 32.mp4 3.48MB 83.mp4 3.48MB 50.mp4 3.45MB 72.mp4 3.45MB 38.mp4 3.43MB共 355 条
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王TYthon2022-01-25下载不了，每次下载一半就失败
