# A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble

arXiv: https://arxiv.org/abs/2107.13355 · HTML: https://mlsanity.com/paper/2107.13355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Real-time Distracted Driver Posture Classification](https://mlsanity.com/paper/1706.09498/) (2017)
- [Driver Distraction Identification with an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/1901.09097/) (2019)
- [Yoga-82: A New Dataset for Fine-grained Classification of Human Poses](https://mlsanity.com/paper/2004.10362/) (2020)
- [A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM](https://mlsanity.com/paper/1911.09512/) (2019)

## Built on by (most cited first, 2 of 2)

- [Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques](https://mlsanity.com/paper/2505.01973/) (2025)
- [A Two-stage Transformer Framework for Temporal Localization of Distracted Driver Behaviors](https://mlsanity.com/paper/2603.21048/) (2026)

## Abstract (arXiv)

As the proportion of road accidents increases each year, driver distraction continues to be an important risk component in road traffic injuries and deaths. The distractions caused by the increasing use of mobile phones and other wireless devices pose a potential risk to road safety. Our current study aims to aid the already existing techniques in driver posture recognition by improving the performance in the driver distraction classification problem. We present an approach using a genetic algorithm-based ensemble of six independent deep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, Vanilla CNN, Modified DenseNet, and InceptionV3 + BiLSTM. We test it on two comprehensive datasets, the AUC Distracted Driver Dataset, on which our technique achieves an accuracy of 96.37%, surpassing the previously obtained 95.98%, and on the State Farm Driver Distraction Dataset, on which we attain an accuracy of 99.75%. The 6-Model Ensemble gave an inference time of 0.024 seconds as measured on our machine with Ubuntu 20.04(64-bit) and GPU as GeForce GTX 1080.