ElizabethBV/MSV_PG

Dataset Card for Dataset Name

Multi-Scale Violence and Public Gathering (MSV-PG) Dataset

This dataset classifies public events along two axes: the size of the crowd observed and the level of perceived violence in the crowd. The videos were annotated to identify temporal segments corresponding to specific behavioral events. Click here for the details of the dataset.

Dataset Details

Classes

• 'natural' – Everyday scenes without significant events.
• 'lpg' – Large peaceful gatherings - Large groups gathered without violence.
• 'lvg' – Large violent gatherings - Widespread violence within a large crowd.
• 'violence' – Small-scale fighting events- Localized violent interactions among a smaller number of individuals.

Dataset Structure

The dataset follows a hierarchical organization:

MSV_PG Dataset/
│── train/
│   ├── natural/
│   │   ├── sample_000000001_1667_0001/
│   │   │   ├── frame_01.jpg
│   │   │   ├── frame_02.jpg
│   │   │   └── ...
│   │   ├── sample_000000002_0882_0028/
│   │   └── ...
│   │
│   ├── lpg/
│   ├── lvg/
│   └── violence/
│
│── val/
│   ├── natural/
│   ├── lpg/
│   ├── lvg/
│   └── violence/
│
│── train_sample_list.csv
│── valid_sample_list.csv
│── README.md

Each sample represents a short temporal segment of a video (e.g., ~2 seconds) and contains a fixed number of frames extracted at a consistent frame rate. All frames within a sample belong to the same class label.

The CSV files act as index files and typically include:

- Path to each sample folder
- Corresponding class label

They facilitate efficient loading of datasets during training and evaluation.

Citation

Elzein A, Basaran E, Yang YD and Qaraqe M (2024) A novel multi-scale violence and public gathering dataset for crowd behavior classification. Front. Comput. Sci. 6:1242690. doi: 10.3389/fcomp.2024.1242690

BibTeX:

@article{elzein2024novel,
  title={A novel multi-scale violence and public gathering dataset for crowd behavior classification},
  author={Elzein, Almiqdad and Basaran, Emrah and Yang, Yin David and Qaraqe, Marwa},
  journal={Frontiers in Computer Science},
  volume={6},
  pages={1242690},
  year={2024},
  publisher={Frontiers Media SA},
  doi= {10.3389/fcomp.2024.1242690}
}