Multi-Class Driver Behavior Image Dataset

Distracted driving-related accidents are a critical global issue, especially as road traffic increases in densely populated areas. To address the challenge of driver distraction, we introduce a novel dataset that supports the development of real-time monitoring and detection systems by capturing authentic driver behaviors. Collected in Ashulia, Dhaka, Bangladesh, in October 2024, this dataset includes images captured under real-world driving conditions within both private vehicles and public buses. The photos were taken using personal mobile phones, ensuring a realistic and diverse set of visual data.

This dataset spans a wide range of driving behaviors, including safe driving, turning, texting, talking on the phone, and other potentially risky behaviors, such as drowsy driving. By depicting these behaviors in everyday driving scenarios, the dataset serves as a valuable resource for training and evaluating models designed to detect unsafe driving practices in real-time.The dataset includes high-resolution photos taken inside public buses and personal cars in Ashulia, Dhaka, Bangladesh, under actual driving circumstances. The photographs, which were taken using the cameras on personal cell phones, offer a genuine and varied collection of visual information under normal driving circumstances. The following five behavioral classes comprise the dataset:
I. Safe Driving: Images showing a driver who seems to be paying attention to the road, both hands on the wheel, and concentrated or 1 hand on the steering wheel and other on the gear stick. This is the perfect example of driving without distractions.
II. Turning: Photographs that show drivers changing direction during turns by moving their heads or full bodies. This behavior is crucial for figuring out how focused the driver is on everyday tasks like rotating the steering wheel.
III. Texting Phone: Pictures of drivers using their phones, whether it is to type messages or to interact with the screen. Since texting and driving is one of the main causes of distracted driving, this training is very important for identifying it.
IV. Talking Phones: When drivers talk on their phones or hold them up to their ears while driving a vehicle. This category aids in identifying actions connected to phone talks, which are another frequent source of interruptions.
V. Others: Contains any actions that go against safe driving practices, like drinking water or anything while driving, sleeping while driving, or talking with someone behind while driving.
Relevant photos are included in each session, and they differ in terms of vehicle type and illumination to represent the variety of driving situations found in the real world. Because the images are unprocessed and unannotated, there is freedom in how machine learning applications pre-process them.

驾驶分心相关事故是全球范围内的一项关键问题，尤其是在人口密集地区道路交通日益增长的情况下。为应对驾驶员分心的挑战，我们推出了一项新颖的数据集，该数据集通过捕捉真实的驾驶行为，支持实时监控和检测系统的开发。该数据集于2024年10月在孟加拉国达卡市的阿什乌利亚收集，包括在真实驾驶条件下从私人车辆和公共汽车中捕获的图像。照片使用个人手机拍摄，确保了视觉数据的真实性和多样性。该数据集涵盖了广泛的驾驶行为，包括安全驾驶、转弯、发短信、打电话以及其他潜在的冒险行为，如疲劳驾驶。通过描绘这些行为在日常驾驶场景中的表现，该数据集成为训练和评估旨在实时检测不安全驾驶行为的模型的有价值资源。数据集包括在孟加拉国达卡市阿什乌利亚的公共汽车和个人汽车内，在真实驾驶情况下拍摄的高分辨率照片。这些使用个人手机相机拍摄的照片，提供了在正常驾驶条件下真实且多样化的视觉信息。数据集包含以下五个行为类别：

I. 安全驾驶：展示驾驶员似乎专注于道路，双手握方向盘，集中注意力或一手握方向盘，另一手握变速杆的图像。这是无分心驾驶的典范。

II. 转弯：显示驾驶员在转弯时通过头部或全身移动来改变方向的照片。这种行为对于确定驾驶员在日常任务（如旋转方向盘）上的专注程度至关重要。

III. 发短信：展示驾驶员使用手机的图片，无论是发短信还是与屏幕互动。鉴于发短信和驾驶是分心驾驶的主要原因之一，这种训练对于识别它非常重要。

IV. 打电话：当驾驶员在驾驶车辆时打电话或将手机贴近耳朵。这一类别有助于识别与电话交谈相关的行为，这是另一种常见的干扰来源。

V. 其他：包含任何违反安全驾驶规范的行为，如驾驶时喝水或做其他事情，驾驶时睡觉，或驾驶时与车后的人交谈。

每个会话都包含相关的照片，这些照片在车辆类型和照明方面有所不同，以代表现实世界中发现的多种驾驶情况。由于图像未经处理和标注，机器学习应用在预处理它们时具有灵活性。

搜集汇总

背景与挑战

背景概述

该数据集包含在孟加拉国真实驾驶场景下采集的驾驶员行为图像，涵盖安全驾驶、转向、使用手机等5类行为，图像数据多样且未经处理，适用于开发分心驾驶检测模型。