Baracca: a Multimodal Dataset for Anthropometric Measurements in Automotive

The recent spread of depth sensors has enabled new methods to automatically estimate anthropometric measurements, in place of manual procedures or expensive 3D scanners. Generally, the use of depth data is limited by the lack of depth-based public datasets containing accurate anthropometric annotations. Therefore, in this paper we propose a new dataset, called Baracca, specifically designed for the automotive context, including in-car and outside views. The dataset is multimodal: it has been acquired with synchronized depth, infrared, thermal and RGB cameras in order to deal with the requirements imposed by the automotive context. In addition, we propose several baselines to test the challenges of the presented dataset and provide considerations for future work.

[1]  Angkoon Phinyomark,et al.  The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface , 2014 .

[2]  Kathleen M. Robinette,et al.  The CAESAR project: a 3-D surface anthropometry survey , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[3]  Hans-Peter Seidel,et al.  A Statistical Model of Human Pose and Body Shape , 2009, Comput. Graph. Forum.

[4]  Dong Liu,et al.  Deep High-Resolution Representation Learning for Human Pose Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Bernt Schiele,et al.  Building statistical shape spaces for 3D human modeling , 2015, Pattern Recognit..

[6]  Ricardo Matsumura de Araújo,et al.  Person Identification Using Anthropometric and Gait Data from Kinect Sensor , 2015, AAAI.

[7]  Hans-Peter Seidel,et al.  MovieReshape: tracking and reshaping of humans in videos , 2010, ACM Trans. Graph..

[8]  Rita Cucchiara,et al.  Hand Gestures for the Human-Car Interaction: The Briareo Dataset , 2019, ICIAP.

[9]  B Das,et al.  Industrial workstation design: a systematic ergonomics approach. , 1996, Applied ergonomics.

[10]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[11]  Jean-Luc Dugelay,et al.  Bag of soft biometrics for person identification , 2010, Multimedia Tools and Applications.

[12]  Rita Cucchiara,et al.  Hands on the wheel: A Dataset for Driver Hand Detection and Tracking , 2018, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[17]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[18]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[19]  Ilker Ercan,et al.  Anthropometric Measurements Usage in Medical Sciences , 2015, BioMed research international.

[20]  Rita Cucchiara,et al.  Manual Annotations on Depth Maps for Human Pose Estimation , 2019, ICIAP.

[21]  Michael J. Black,et al.  Home 3D body scans from noisy image and range data , 2011, 2011 International Conference on Computer Vision.

[22]  Fabio Ruggiero,et al.  Height Estimation from a Single Camera View , 2012, VISAPP.

[23]  Andreas Kolb,et al.  Kinect range sensing: Structured-light versus Time-of-Flight Kinect , 2015, Comput. Vis. Image Underst..

[24]  Pascal Fua,et al.  Gravity as a Reference for Estimating a Person’s Height From Video , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[25]  Luc Van Gool,et al.  Efficient Model-Free Anthropometry from Depth Data , 2017, 2017 International Conference on 3D Vision (3DV).

[26]  Rita Cucchiara,et al.  Embedded recurrent network for head pose estimation in car , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[27]  Ye-Peng Guan.  Unsupervised human height estimation from a single image , 2009 .

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Rita Cucchiara,et al.  Fast and Accurate Facial Landmark Localization in Depth Images for In-Car Applications , 2017, ICIAP.