DriveAHead — A Large-Scale Driver Head Pose Dataset

Schwarz, Anke; Haurilet, Monica; Martínez, Manuel Martínez; Stiefelhagen, Rainer

doi:10.1109/cvprw.2017.155

Cited by 61 publications

(35 citation statements)

References 30 publications

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“…There is an abundance of publicly available image-based head pose datasets dating back nearly two decades [3]- [14] (see Table I).…”

Section: Related Workmentioning

confidence: 99%

“…Acquisition scenario covers the circumstances under which the acquisition of the head pose takes place. The most important distinction is between in-laboratory [4,7,8,11]- [13] vs. in-the-wild [5,6,9,10,14] acquisition. While the former restricts the data by defining a rather well-defined, static environment, the latter offers more variety through being acquired in unconstrained environments such as outside, thus covering many challenges like differing illumination and variable background.…”

Section: Related Workmentioning

confidence: 99%

“…For manual annotations, human experts annotate the image data according to a label specification [4]. Automatic annotations can be divided into data-based annotations, computed by algorithms on the image data [8,13], and sensorbased annotations, which in turn use an additional hardware sensor for obtaining the head pose for each image [7,11,14].…”

Section: Related Workmentioning

confidence: 99%

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

Roth

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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We introduce DD-Pose, the Daimler TU Delft Driver Head Pose Benchmark, a large-scale and diverse benchmark for image-based head pose estimation and driver analysis. It contains 330k measurements from multiple cameras acquired by an in-car setup during naturalistic drives. Large out-of-plane head rotations and occlusions are induced by complex driving scenarios, such as parking and driver-pedestrian interactions. Precise head pose annotations are obtained by a motion capture sensor and a novel calibration device. A high resolution stereo driver camera is supplemented by a camera capturing the driver cabin. Together with steering wheel and vehicle motion information, DD-Pose paves the way for holistic driver analysis. Our experiments show that the new dataset offers a broad distribution of head poses, comprising an order of magnitude more samples of rare poses than a comparable dataset. By an analysis of a state-of-the-art head pose estimation method, we demonstrate the challenges offered by the benchmark. The dataset and evaluation code are made freely available to academic and non-profit institutions for non-commercial benchmarking purposes.

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“…There is an abundance of publicly available image-based head pose datasets dating back nearly two decades [3]- [14] (see Table I).…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Roth

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…Video-based methods are comprised in the survey conducted in (Murphy-Chutorian and Trivedi, 2009). New approaches based on depth data have been proposed in (Fanelli et al, 2011;Fanelli et al, 2013;Meyer et al, 2015;Papazov et al, 2015;Borghi et al, 2017), as well as combined methods using both RGB and depth data (Baltrušaitis et al, 2012;Saeed and Al-Hamadi, 2015), or IR and depth data (Schwarz et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

“…The proposed regression neural network, POSEidon, integrated depth with motion features and appearance. On the other hand, (Schwarz et al, 2017) presented a deep learning method for HPE under driving conditions, by fusing IR and depth data with cross-stitch units.…”

Section: Related Workmentioning

confidence: 99%

Combined Framework for Real-time Head Pose Estimation using Facial Landmark Detection and Salient Feature Tracking

Barros

Garcia

Mirbach

et al. 2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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This paper presents a novel approach to address the head pose estimation (HPE) problem in real world and demanding applications. We propose a new framework that combines the detection of facial landmarks with the tracking of salient features within the head region. That is, rigid facial landmarks are detected from a given face image, while at the same time, salient features are detected within the head region. The 3D coordinates of both set of features result from their intersection on a simple geometric head model (e.g., cylinder or ellipsoid). We then formulate the HPE problem as a perspective-n-point problem that we separately solve by minimizing the reprojection error of each 3D features set and their corresponding facial or salient features in the next face image. The resulting head pose estimations are then combined using Kalman Filter, which allows us to take advantage of the high accuracy when using facial landmarks while enabling us to handle extreme head poses by using salient features. Results are comparable to those from the related literature, with the advantage of being robust under real world situations that might not be covered in the evaluated datasets.

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