Head Pose Estimation for an Omnidirectional Camera Using a Convolutional Neural Network

Yamaura, Yusuke; Tsuboshita, Yukihiro; Onishi, Toshikazu

doi:10.1109/ivmspw.2018.8448756

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…• Surveillance and Safety: Head pose estimation in surveillance video images is an important task in computer vision because it tracks the visual attention and provides insight on human behavioural intentions [21,22]. Systems for direct an automated surveillance network have been proposed in [23,24].…”

Section: Motivationmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the field by proposing a classification of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the different ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic.

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Section: Motivationmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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“…This favors the leverage of the facial feature-richness and suitable, widely available training datasets. However, in uncontrolled application scenarios [24], [25], [26] head orientations are likely to surpass the narrow angle range that most methods are trained for and, consequently, produce random and inaccurate head pose predictions. In view of extending the prediction to the full area of rotation range, the current state of research is challenged by two key limitations.…”

Section: Introductionmentioning

confidence: 99%

Toward Robust and Unconstrained Full Range of Rotation Head Pose Estimation

Hempel,

Abdelrahman,

Al-Hamadi

2024

IEEE Trans. on Image Process.

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Estimating the head pose of a person is a crucial problem for numerous applications that is yet mainly addressed as a subtask of frontal pose prediction. We present a novel method for unconstrained end-to-end head pose estimation to tackle the challenging task of full range of orientation head pose prediction. We address the issue of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This allows to efficiently learn full rotation appearance and to overcome the limitations of the current state-of-the-art. Together with new accumulated training data that provides full head pose rotation data and a geodesic loss approach for stable learning, we design an advanced model that is able to predict an extended range of head orientations. An extensive evaluation on public datasets demonstrates that our method significantly outperforms other state-of-the-art methods in an efficient and robust manner, while its advanced prediction range allows the expansion of the application area. We open-source our training and testing code along with our trained models: https://github.com/thohemp/6DRepNet360.

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All In One Network for Driver Attention Monitoring

Dai

Zhang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Head Pose Estimation for an Omnidirectional Camera Using a Convolutional Neural Network

Cited by 3 publications

References 13 publications

Deep Learning for Head Pose Estimation: A Survey

Deep Learning for Head Pose Estimation: A Survey

Toward Robust and Unconstrained Full Range of Rotation Head Pose Estimation

All In One Network for Driver Attention Monitoring

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