DD-Pose - A large-scale Driver Head Pose Benchmark

Roth, Markus; Gavrila, Dariu M.

doi:10.1109/ivs.2019.8814103

Cited by 25 publications

(17 citation statements)

References 21 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Daimler TU Delft Driver Head Pose (DD-Pose) Benchmark [30] utilizes one high-resolution stereo camera (2,048 × 2,048) capturing the driver's face, and a wide-angle RGB camera recording from the backside of the driver. The authors used a 3D motion capturing system and provide rotation and translation labels for the driver's head.…”

Section: A Databases Focusing On Head Pose Estimationmentioning

confidence: 99%

“…Our primary objective was to have a head pose label for each of the frames in our corpus. Alternative systems to track head pose in actual vehicle include motion capture systems [27], [30], and IMU measures [28]. The IMU systems are sensitive to drifts in the measurement and, hence, the recording of more than 10 minutes is not possible without significant drop in the reliability.…”

Section: B Multimodal Sensorsmentioning

confidence: 99%

“…The IMU systems are sensitive to drifts in the measurement and, hence, the recording of more than 10 minutes is not possible without significant drop in the reliability. Passive Motion capture (Mo-Cap) systems with retroreflective markers have gained popularity in recent studies [27], [30]. The reliability depends on the number of markers, the frame rate and resolution of the camera, and environmental factors such as background illumination and occlusion.…”

Section: B Multimodal Sensorsmentioning

confidence: 99%

“…A Mo-Cap system requires a sophisticated setup, so it is more appropriate for data collections in car-simulators (e.g., AutoPOSE corpus [32]). There are very few examples of motion capture systems in naturalistic data collections in real road [27], [29], [30]. Vibrations in the car and movements of the markers can have a big impact on the recordings.…”

Section: B Multimodal Sensorsmentioning

confidence: 99%

See 3 more Smart Citations

The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

Jha

Marzban

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.

show abstract

Section: A Databases Focusing On Head Pose Estimationmentioning

confidence: 99%

Section: B Multimodal Sensorsmentioning

confidence: 99%

Section: B Multimodal Sensorsmentioning

confidence: 99%

Section: B Multimodal Sensorsmentioning

confidence: 99%

See 2 more Smart Citations

The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

Jha

Marzban

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…This information together with the interior design of the cabin help identifying where the driver is paying attention, as in DrivFace dataset [6]. In addition, datasets such as DriveAHead [32] and DD-Pose [29] provide head pose annotations of yaw, pitch and roll angles.…”

Section: Related Workmentioning

confidence: 99%

Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

Köpüklü¹,

Zheng²,

Xu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, which leads to an 'open set recognition' problem. Accordingly, instead of recognizing a set of anomalous actions that are commonly defined by previous dataset providers, in this work, we propose a contrastive learning approach to learn a metric to differentiate normal driving from anomalous driving. For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving. Our method reaches 0.9673 AUC on the test set, demonstrating the effectiveness of the contrastive learning approach on the anomaly detection task. Our dataset, codes and pre-trained models are publicly available 1 .

show abstract