# Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion

arXiv: https://arxiv.org/abs/2408.02033 · HTML: https://mlsanity.com/paper/2408.02033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 4 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Learning Individual Styles of Conversational Gesture](https://mlsanity.com/paper/1906.04160/) (2019)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Speech2Face: Learning the Face Behind a Voice](https://mlsanity.com/paper/1905.09773/) (2019)
- [A Multi-viewpoint Outdoor Dataset for Human Action Recognition](https://mlsanity.com/paper/2110.04119/) (2021)
- [Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning](https://mlsanity.com/paper/2209.00990/) (2022)
- [A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation](https://mlsanity.com/paper/2201.05314/) (2022)
- [Crime scene classification from skeletal trajectory analysis in surveillance settings](https://mlsanity.com/paper/2207.01687/) (2022)
- [DeepSafety:Multi-level Audio-Text Feature Extraction and Fusion Approach for Violence Detection in Conversations](https://mlsanity.com/paper/2206.11822/) (2022)

## Built on by (most cited first, 1 of 1)

- [Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding](https://mlsanity.com/paper/2506.12336/) (2025)

## Abstract (arXiv)

This paper proposes a hybrid fusion-based deep learning approach based on two different modalities, audio and video, to improve human activity recognition and violence detection in public places. To take advantage of audiovisual fusion, late fusion, intermediate fusion, and hybrid fusion-based deep learning (HFBDL) are used and compared. Since the objective is to detect and recognize human violence in public places, Real-life violence situation (RLVS) dataset is expanded and used. Simulating results of HFBDL show 96.67% accuracy on validation data, which is more accurate than the other state-of-the-art methods on this dataset. To showcase our model's ability in real-world scenarios, another dataset of 54 sounded videos of both violent and non-violent situations was recorded. The model could successfully detect 52 out of 54 videos correctly. The proposed method shows a promising performance on real scenarios. Thus, it can be used for human action recognition and violence detection in public places for security purposes.