Estimating Driver's Point-of-Gaze in Traffic Scenes using a Dashboard-Mounted Camera System

Основні поняття

We propose a novel convolutional network that can estimate a driver's point-of-gaze on a traffic scene using a pair of cameras mounted on the windshield and dashboard of a car.

Анотація

The paper presents a method for estimating a driver's point-of-gaze in traffic scenes using a dashboard-mounted camera system. The key contributions are:

A novel convolutional network architecture called Drivers' Points-of-Gaze Estimation Network (DPEN) that takes as input the scene image and the driver's face image, and outputs the estimated point-of-gaze on the scene image.

A camera calibration module within DPEN that can compute an embedding vector representing the spatial configuration between the driver and the camera system, improving the overall network's performance.

A large-scale dataset called Drivers' Points-of-Gaze (DPoG) with over 143,000 annotated frames of synchronized scene, face, and gaze data collected from real driving sessions. The dataset provides valuable insights into driver attention and behavior.

Experiments on the DPoG dataset show that DPEN outperforms various baseline methods, achieving a mean prediction error of 29.69 pixels, which is relatively small compared to the 1280×720 resolution of the scene camera. The camera calibration module and the use of both scene and face images are crucial for DPEN's superior performance.

Статистика

The driver's gaze point is within 29.69 pixels on average of the predicted point on the 1280×720 scene image.
The driver's gaze point is within 2.95 degrees on average of the predicted point based on the field-of-view of the scene camera.

Цитати

"We propose a novel convolutional network that simultaneously analyzes the image of the scene and the image of the driver's face. This network has a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system."
"Experiments on this dataset show that the proposed method outperforms various baseline methods, having the mean prediction error of 29.69 pixels, which is relatively small compared to the 1280×720 resolution of the scene camera."

Ключові висновки, отримані з

Driver Attention Tracking and Analysis

by Dat Viet Tha... о arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07122.pdf

Глибші Запити

How can the estimated driver's point-of-gaze be used to improve driver assistance systems and enhance road safety

The estimated driver's point-of-gaze can be a valuable input for improving driver assistance systems and enhancing road safety in several ways. Firstly, by knowing where the driver is looking, the system can provide timely alerts and warnings to the driver about potential hazards or critical road situations that may require immediate attention. For example, if the driver fails to notice a pedestrian crossing the road or a vehicle suddenly braking ahead, the system can alert the driver to take corrective action, thereby reducing the risk of accidents.
Secondly, the point-of-gaze information can be used to personalize the driving experience and optimize the delivery of information to the driver. By understanding the driver's visual focus, the system can adjust the presentation of information on the dashboard or in the heads-up display to minimize distractions and ensure that important information is easily accessible to the driver without causing cognitive overload.
Furthermore, the data on driver attention and gaze patterns can be analyzed to identify trends and patterns that may indicate driver fatigue, distraction, or other risky behaviors. This information can be used to develop proactive interventions, such as suggesting rest breaks or adjusting the driving environment to reduce distractions and improve overall driver focus.

What are the potential limitations of the proposed method, and how could it be further improved to handle more challenging driving scenarios

While the proposed method for estimating the driver's point-of-gaze shows promising results, there are potential limitations that need to be addressed for handling more challenging driving scenarios. One limitation is the reliance on synchronized video data from multiple cameras, which may not always be feasible in real-world settings or may introduce complexities in calibration and synchronization. To improve robustness, the system could benefit from additional sensors or data sources to enhance accuracy and reliability.
Another limitation is the assumption of a fixed relationship between the driver and the camera system, which may not hold true in dynamic driving environments with varying distances and angles. Incorporating real-time adaptive calibration mechanisms based on continuous feedback from the driver's movements and the scene dynamics could enhance the system's adaptability and performance in diverse driving conditions.
To handle more challenging scenarios, the method could also be enhanced by integrating advanced machine learning techniques, such as reinforcement learning or attention mechanisms, to capture complex driver behaviors and attention patterns more effectively. Additionally, exploring the use of 3D scene reconstruction and depth estimation techniques could provide valuable depth cues for improving the accuracy of gaze estimation in environments with varying depths and occlusions.

How can the insights from the driver attention statistics in the DPoG dataset be leveraged to design better road infrastructure and traffic management systems

The insights from the driver attention statistics in the DPoG dataset can be leveraged to design better road infrastructure and traffic management systems by understanding how drivers interact with their environment and identifying areas for improvement. By analyzing the gaze patterns and attentional focus of drivers in different driving scenarios, traffic engineers and urban planners can gain valuable insights into the effectiveness of road signage, intersection layouts, and traffic flow management strategies.
For example, by identifying common gaze zones and attention hotspots for drivers, road infrastructure can be optimized to enhance visibility, reduce cognitive load, and improve decision-making at critical points on the road. This could involve redesigning road signs, optimizing traffic signal placement, or implementing better lighting to draw attention to important information and potential hazards.
Furthermore, the data on driver attention can inform the design of intelligent transportation systems that prioritize safety and efficiency. By integrating real-time gaze tracking technology with traffic monitoring systems, traffic signals, and autonomous vehicles, it is possible to create a more responsive and adaptive transportation network that prioritizes safety, minimizes congestion, and enhances the overall driving experience for road users.