3D head pose estimation in monocular video sequences by sequential camera self-calibration

Marras, Ioannis; Nikolaidis, Nikos; Pitas, Ioannis

doi:10.1109/mmsp.2009.5293570

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous head-pose estimation approaches can be grouped into feature-based, appearance-based, or model-based approaches [6][7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

Novel Backprojection Method for Monocular Head Pose Estimation

Ju¹,

Shin²,

Klette³

2013

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

Estimating a driver's head pose is an important task in driver-assistance systems because it can provide information about where a driver is looking, thereby giving useful cues about the status of the driver (i.e., paying proper attention, fatigued, etc.). This study proposes a system for estimating the head pose using monocular images, which includes a novel use of backprojection. The system can use a single image to estimate a driver's head pose at a particular time stamp, or an image sequence to support the analysis of a driver's status. Using our proposed system, we compared two previous pose estimation approaches. We introduced an approach for providing ground-truth reference data using a mannequin model. Our experimental results demonstrate that the proposed system provides relatively accurate estimations of the yaw, tilt, and roll angle. The results also show that one of the pose estimation approaches (perspective-n-point, PnP) provided a consistently better estimate compared to the other (pose from orthography and scaling with iterations, POSIT) using our proposed system.

show abstract

“…Previous head-pose estimation approaches can be grouped into feature-based, appearance-based, or model-based approaches [6][7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

Novel Backprojection Method for Monocular Head Pose Estimation

Ju¹,

Shin²,

Klette³

2013

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In [13] and [7] pose estimation is performed in a model based framework. When applied to articulated objects such as the human hand, this allows estimation of each joint angle individually.…”

Section: Introductionmentioning

confidence: 99%

Generalised Pose Estimation Using Depth

Hadfield

Bowden

2012

Trends and Topics in Computer Vision

View full text Add to dashboard Cite

Abstract. Estimating the pose of an object, be it articulated, deformable or rigid, is an important task, with applications ranging from HumanComputer Interaction to environmental understanding. The idea of a general pose estimation framework, capable of being rapidly retrained to suit a variety of tasks, is appealing. In this paper a solution is proposed requiring only a set of labelled training images in order to be applied to many pose estimation tasks. This is achieved by treating pose estimation as a classification problem, with particle filtering used to provide non-discretised estimates. Depth information extracted from a calibrated stereo sequence, is used for background suppression and object scale estimation. The appearance and shape channels are then transformed to Local Binary Pattern histograms, and pose classification is performed via a randomised decision forest. To demonstrate flexibility, the approach is applied to two different situations, articulated hand pose and rigid head orientation, achieving 97% and 84% accurate estimation rates, respectively.

show abstract

3D head pose estimation in monocular video sequences by sequential camera self-calibration

Cited by 2 publications

References 16 publications

Novel Backprojection Method for Monocular Head Pose Estimation

Novel Backprojection Method for Monocular Head Pose Estimation

Generalised Pose Estimation Using Depth

Contact Info

Product

Resources

About