Human Gaze Tracking With An Active Multi-Camera System

Manolova, Agata; Panev, Stanislav; Tonchev, Krasimir

doi:10.1007/978-3-319-13386-7_14

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Feature-based estimation [ 13 , 19 , 20 , 21 , 22 ], a method of gaze estimation, mainly uses unique features that have geometric relationships with the eye. Existing research studies have focused on objects that have a strong visual influences on images.…”

Section: Related Workmentioning

confidence: 99%

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Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images

Lee

Yoo

et al. 2022

Sensors

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Gaze is an excellent indicator and has utility in that it can express interest or intention and the condition of an object. Recent deep-learning methods are mainly appearance-based methods that estimate gaze based on a simple regression from entire face and eye images. However, sometimes, this method does not give satisfactory results for gaze estimations in low-resolution and noisy images obtained in unconstrained real-world settings (e.g., places with severe lighting changes). In this study, we propose a method that estimates gaze by detecting eye region landmarks through a single eye image; and this approach is shown to be competitive with recent appearance-based methods. Our approach acquires rich information by extracting more landmarks and including iris and eye edges, similar to the existing feature-based methods. To acquire strong features even at low resolutions, we used the HRNet backbone network to learn representations of images at various resolutions. Furthermore, we used the self-attention module CBAM to obtain a refined feature map with better spatial information, which enhanced the robustness to noisy inputs, thereby yielding a performance of a 3.18% landmark localization error, a 4% improvement over the existing error and A large number of landmarks were acquired and used as inputs for a lightweight neural network to estimate the gaze. We conducted a within-datasets evaluation on the MPIIGaze, which was obtained in a natural environment and achieved a state-of-the-art performance of 4.32 degrees, a 6% improvement over the existing performance.

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Section: Related Workmentioning

confidence: 99%

“…Some researchers added multiple cameras to compute and focus head movements in multiple directions, extending the influence of eye information and head posture information [ 20 , 21 ]. Head pose has a significant effect on the gaze and requires many restrictions.…”

Section: Related Workmentioning

confidence: 99%

Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images

Lee

Yoo

et al. 2022

Sensors

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“…Valenti and Gevers [6] enhanced gaze estimation using prior knowledge regarding a scene to construct a probability distribution between the scene and gaze points. Manolova et al [10] added an additional fixed camera into a multi-camera system to determine the position of the human face and components such as the eyes, and used this information to correct the eye landmarks and head pose. Lai et al [20] used glints and contour features in a 3D gaze tracking method that directly estimates the line of sight in a 3D space.…”

Section: Related Workmentioning

confidence: 99%

“…These methods have achieved promising results, but often require multiple cameras and changes in illumination and types of light sources tend to reduce accuracy. Feature-based approaches [8][9][10] attempt to extract distinctive features such as contours, eye corners, and corneal reflections or glints. Such methods work well in controlled indoor environments.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Gaze Tracking Using Convolutional Long Short-Term Memory Networks

Vo¹,

Kong²

2022

IJFIS

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This paper presents convolutional long short-term memory (C-LSTM) networks for improving the accuracy of gaze estimation. C-LSTM networks learn temporal variations in facial features while a human subject looks at objects displayed on a monitor screen equipped with a live camera. Given a sequence of input video frames, a set of convolutional layers individually extracts facial features from regions of interest such as the left eye, right eye, face, and face grid of the subject. Subsequently, an LSTM network encodes the relationships between changes in facial features over time and the position of the gaze point. C-LSTM networks are trained on a set of input-output data pairs of facial features and corresponding positions of the gaze point, and the spatial coordinates of the gaze point are determined based on the facial features of the current frame and information from previous frames to improve the accuracy of gaze estimation. Experiment results demonstrate that the proposed scheme achieves significant improvement of gaze tracking performance with average gaze estimation errors of 0.82 and 0.92 cm in the horizontal and vertical axes, respectively, on the GazeCapture dataset and an average angular error of 6.1 • on the MPIIGaze dataset.

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“…Existing virtual reality (VR) games (e.g., Oculus Rift, HTC Vive, and PlayStation VR) already provide quite an immersive user experience, which is also a main user requirement when selecting a gaming application. Intelligent computer vision techniques have also been gaining strong research focus because of their potential to realize personalized applications based on human activity, object or scene recognition, of particular interest to the area of assisted living and eHealth, and even business modeling [2][3][4][5]. Such emerging technologies and platforms, are capable of sensing, digitization, transmission and replication of human-related information and can be seen as a first step towards a multisensory human-bond data communication (HBC) framework.…”

Section: Introductionmentioning

confidence: 99%

Game and Multisensory Driven Ecosystem to an Active Lifestyle

Pnevmatikakis

Akker

Kyriazakos

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The trends in healthcare are continuously evolving towards a virtually rich personalized experience that involves human-to-human (H2H), human-to-machine (H2M) and machine-to-machine (M2M) interactions. This article proposes a platform that fosters an ecosystem of games and applies them to real-life situations to motivate an active lifestyle in elderly and healthimpacted adults. The platform facilitates behavioral change through numerous games and applications that contribute to active living by introducing awards that can be earned upon reaching goals and can be redeemed in other applications of the GOAL ecosystem. The platform consists of core functionalities (account management, virtual reward system and activity recognition); tools for social inclusion (the social marketplace) and tools for healthy behavior (the goal setting service and the motivational agent). Multisensory technology has been proposed as means to enhance the evaluation on the achieved degree of user motivation. The platform applications are interactive games functioning as GOAL Coin Generators and/or Spenders.

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Human Gaze Tracking With An Active Multi-Camera System

Cited by 6 publications

References 12 publications

Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images

Improved Feature-Based Gaze Estimation Using Self-Attention Module and Synthetic Eye Images

Enhanced Gaze Tracking Using Convolutional Long Short-Term Memory Networks

Game and Multisensory Driven Ecosystem to an Active Lifestyle

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