Introduction:
Driving distractions such as texting, eating, or using a phone, contribute largely to road accidents worldwide. Identifying and addressing these distractions in real time is crucial for enhancing road safety and reducing the risk of accidents. By accurately identifying driver activities in real time, this project aims to improve road safety by alerting drivers or triggering safety mechanisms when potential distractions are detected.
Objective:
Given a dataset of images captured inside vehicles, the system must classify activities such as texting, eating, talking on the phone, applying makeup, or reaching behind the driver's seat. The primary objective is to design and implement a deep learning model using computer vision techniques and analyze these images. Additionally, the idea is to annotate and detect objects within the vehicle cabin, such as smartphones, food items, or makeup accessories. This task can provide additional context to driver activities and help identify potential distractions more accurately.
Dataset Description:
The dataset comprises about 22424 images captured inside vehicles, each describing a driver engaged in various activities. These activities include safe driving, texting, talking on the phone, operating the radio, drinking, reaching behind, hair and makeup tasks, and conversing with a passenger. To preserve the integrity of the computer vision problem, metadata such as creation dates has been removed, and the dataset has been split based on drivers. Additionally, to discourage hand labeling, the test dataset includes processed images that are resized and ignored during evaluation.
Kaggle competition URL: https://www.kaggle.com/competitions/state-farm-distracted-driver-detection/overview