Real Life Violence Detection Dataset

This repository contains the "Real Life Violence Dataset" used for training video violence detection models, along with preprocessed versions.

Associated Model: ross-dev/violence-transformer Original Code Repository: ross-sec/violence-transformer

Dataset Description

The dataset consists of video clips categorized into two classes: "Violence" and "NonViolence". It includes real-world scenarios. Due to the nature of the content, user discretion is advised.

Dataset Structure

This repository contains two main ways to access the data:

Raw Video Files:
- Real Life Violence Dataset/Violence/: Contains .mp4, .avi, etc., files depicting violent scenarios.
- Real Life Violence Dataset/NonViolence/: Contains .mp4, .avi, etc., files depicting non-violent scenarios.
Preprocessed NumPy Arrays:
- features.npy: A NumPy array containing sequences of preprocessed frames. Shape: (num_videos, sequence_length, height, width, channels).
- labels.npy: A NumPy array containing the corresponding labels (0 for NonViolence, 1 for Violence). Shape: (num_videos,).
- video_files_paths.npy (Optional): May contain paths to the original video files corresponding to the features/labels.

Preprocessing (for `.npy` files)

The features.npy and labels.npy files were generated using the DatasetCreator class within the associated model's training script (train_transformer.py). The process involves:

Extracting SEQUENCE_LENGTH (e.g., 16) frames evenly spaced throughout each video.
Resizing each frame to IMAGE_HEIGHT x IMAGE_WIDTH (e.g., 64x64).
Normalizing pixel values to the range [0, 1].
Skipping videos that couldn't be processed or didn't have enough frames.

Refer to the DatasetCreator.frames_extraction method in the code repository for the exact implementation details.

How to Use

With Raw Videos: You can directly load and process the video files from the Real Life Violence Dataset/ subdirectories using libraries like OpenCV or PyAV for custom preprocessing pipelines.
With Preprocessed Features: Load the .npy files using NumPy:
```
import numpy as np

features = np.load("features.npy")
labels = np.load("labels.npy")

print("Features shape:", features.shape)
print("Labels shape:", labels.shape)
```
These arrays can be directly used to create PyTorch or TensorFlow datasets for training models like the associated VideoTransformer.

Citation Information

Please cite the original source of the "Real Life Violence Dataset" if known. If using the preprocessing script or the models from the associated repository, please cite:

@misc{violence_transformer_2025,
  author = {Andre Ross},
  title = {Video Violence Detection Models (MobBiLSTM & Transformer)},
  year = {2025},
  published = {https://github.com/ross-sec/violence-transformer}
}

Disclaimer

The creators of this repository and the associated models are not responsible for the misuse of this dataset. User discretion is strongly advised when working with this data.