Model-free head pose estimation based on shape factorisation and particle filtering

This work forms part of the project Eye-Communicate funded by the Malta Council for Science and Technology through the National Research & Innovation Programme (2012) under Research Grant No. R&I-2012-057.Head pose estimation is essential for several applications and is particularly required for head pose-free eye-gaze tracking where estimation of head rotation permits free head movement during tracking. While the literature is broad, the accuracy of recent vision-based head pose estimation methods is contingent upon the availability of training data or accurate initialisation and tracking of specific facial landmarks. In this paper, we propose a method to estimate the head pose in real time from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, without seeking specific facial landmarks, our method exploits the sparse 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with particle filtering to correct mistracked feature points and improve upon an initial estimation of the 3-dimensional shape during tracking. In comparison with two additional methods, quantitative results obtained through our model- and landmark-free method yield a reduction in the head pose estimation error for a wide range of head rotation angles.peer-reviewe

Recent advances in monocular model-based tracking: a systematic literature review

In this paper, we review the advances of monocular model-based tracking for last ten years period until 2014. In 2005, Lepetit, et. al, [19] reviewed the status of monocular model based rigid body tracking. Since then, direct 3D tracking has become quite popular research area, but monocular model-based tracking should still not be forgotten. We mainly focus on tracking, which could be applied to aug- mented reality, but also some other applications are covered. Given the wide subject area this paper tries to give a broad view on the research that has been conducted, giving the reader an introduction to the different disciplines that are tightly related to model-based tracking. The work has been conducted by searching through well known academic search databases in a systematic manner, and by selecting certain publications for closer examination. We analyze the results by dividing the found papers into different categories by their way of implementation. The issues which have not yet been solved are discussed. We also discuss on emerging model-based methods such as fusing different types of features and region-based pose estimation which could show the way for future research in this subject.Siirretty Doriast

Augmented Reality based 3D Human Hands Tracking from Monocular True Images Using Convolutional Neural Network

Precise modeling of hand tracking from monocular moving camera calibration parameters using semantic cues is an active area of research concern for the researchers due to lack of accuracy and computational overheads. In this context, deep learning based framework, i.e. convolutional neural network based human hands tracking as well as recognizing pose of hands in the current camera frame become active research problem. In addition, tracking based on monocular camera needs to be addressed due to updated technology such as Unity3D engine and other related augmented reality plugins. This research aims to track human hands in continuous frame by using the tracked points to draw 3D model of the hands as an overlay in the original tracked image. In the proposed methodology, Unity3D environment was used for localizing hand object in augmented reality (AR). Later, convolutional neural network was used to detect hand palm and hand keypoints based on cropped region of interest (ROI). Proposed method by this research achieved accuracy rate of 99.2% where single monocular true images were used for tracking. Experimental validation shows the efficiency of the proposed methodology.Peer reviewe

Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model

In the paper, we propose a robust real-time visual odometry in dynamic environments via rigid-motion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a grid-based scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dual-mode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use self-collected dataset containing RGB-D images and motion capture data for ground-truth. We compare our algorithm with state-of-the-art visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments

Human Upper Body Pose Region Estimation

The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe