Datasets
Standard Dataset
Violence Detection in Campus Surveillance
- Citation Author(s):
- Submitted by:
- Senthil Kumar T...
- Last updated:
- Thu, 01/09/2025 - 10:37
- DOI:
- 10.21227/vx78-2y90
- Research Article Link:
- License:
- Categories:
- Keywords:
Abstract
The dataset was specifically created to address the need for violence detection in surveillance systems. It consists of self-recorded videos simulating different types of violent activities relevant to college environments. The dataset is organized into four distinct classes:
Slap
Punch
Kick
Group Violence
Others - Over Crowding, Loitering, Assault, Abuse
Each video is labeled according to its corresponding class to facilitate supervised learning for violence detection models.
DATASET OVERVIEW
Descrip2on
The dataset was specifically created to address the need for violence detec2on in surveillance systems. It
consists of self-recorded videos simula2ng diļ¬erent types of violent ac2vi2es relevant to college
environments. The dataset is organized into four dis2nct classes:
Slap
Punch
Kick
Group Violence
Others - Over Crowding, Loitering, Assault, Abuse
Each video is labeled according to its corresponding class to facilitate supervised learning for violence
detec2on models.
DATA COLLECTION
Source: The videos were recorded in controlled sePngs, mimicking real-life scenarios on a college
campus.
Environment: Indoor and Outdoor
Equipment: Details about the cameras used - DSLR Camera
Actors: Simulated by volunteers following predefined scripts to ensure accuracy and variety.
Trimming: All videos were trimmed to ensure uniform dura2on across the dataset, maintaining
consistency for training and evalua2on purposes.
DATASET STATISTICS
Class Distribu2on:
Slap: 30 videos
Punch: 25 videos
Kick: 30 videos
Group Violence: 50 videos
Over Crowding: 4 videos
Loitering: 4 videos
Assault: 10 videos
Abuse: 7 videos
Dura2on: 1-4 sec of video per class.
Resolu2on: 1920x1080
Video Format: mp4, avi, dav
FILES INCLUDED
folder with an excel of actor images, Name and Roll Number
folders of classes with videos