Fight/Violence Detection in Videos Using 3D CNN
This repository contains a dataset and a 3D CNN (Convolutional Neural Network) model trained to detect fights/violence and non-violence in videos. The model is designed to capture temporal and spatial features to identify violent activities, making it suitable for real-time surveillance and security applications.
Dataset Overview
Dataset Classes: The dataset consists of two classes:
- Violence/Fight: Videos where physical violence is present.
- NoViolence/NoFight: Videos with no physical confrontations.
Data Format:
- The dataset contains videos that are labeled into the above two classes.
- These videos are preprocessed and split into frames that are fed into the 3D CNN model for training and detection.
Model
- 3D CNN Architecture:
- The 3D CNN model is trained to detect patterns across both spatial and temporal dimensions, making it ideal for analyzing video sequences.
- The model uses 3D convolutional layers to capture motion and action-based features, which are crucial for fight/violence detection.
Purpose
The model is developed to detect violent actions in video footage. This system can be deployed in surveillance cameras, security systems, or any environment where fight/violence detection is necessary.
Key Features:
- Fight/Violence Detection:
- The 3D CNN model is trained to recognize fight/violence events in videos, differentiating them from non-violent actions.
- The model processes video sequences to make predictions, utilizing temporal changes and spatial context.
Code and Usage Instructions
Pre-requisites:
- Python 3.8 or higher
- TensorFlow or PyTorch (depending on the implementation)
- OpenCV
- FFmpeg (for video preprocessing)
- Required libraries as mentioned in
requirements.txt
Video Preprocessing:
Extract Frames from Video: The 3D CNN model expects the input as video frames. You can extract frames from videos using the following command:
ffmpeg -i <input-video> -vf fps=25 <output-frame-directory>/frame_%04d.jpg
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no library tag.