# Detection of Distracted Driver using Convolution Neural Network

arXiv: https://arxiv.org/abs/2204.03371 · HTML: https://mlsanity.com/paper/2204.03371/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Large-scale weakly-supervised pre-training for video action recognition](https://mlsanity.com/paper/1905.00561/) (2019)
- [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)
- [The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection](https://mlsanity.com/paper/1602.07119/) (2016)
- [Driver distraction detection and recognition using RGB-D sensor](https://mlsanity.com/paper/1502.00250/) (2015)
- [Drive-Net: Convolutional Network for Driver Distraction Detection](https://mlsanity.com/paper/2006.12586/) (2020)
- [A Pattern Recognition System for Detecting Use of Mobile Phones While Driving](https://mlsanity.com/paper/1408.0680/) (2014)

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

- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)
- [Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks](https://mlsanity.com/paper/2405.17788/) (2024)

## Abstract (arXiv)

With over 50 million car sales annually and over 1.3 million deaths every year due to motor accidents we have chosen this space. India accounts for 11 per cent of global death in road accidents. Drivers are held responsible for 78% of accidents. Road safety problems in developing countries is a major concern and human behavior is ascribed as one of the main causes and accelerators of road safety problems. Driver distraction has been identified as the main reason for accidents. Distractions can be caused due to reasons such as mobile usage, drinking, operating instruments, facial makeup, social interaction. For the scope of this project, we will focus on building a highly efficient ML model to classify different driver distractions at runtime using computer vision. We would also analyze the overall speed and scalability of the model in order to be able to set it up on an edge device. We use CNN, VGG-16, RestNet50 and ensemble of CNN to predict the classes.