Hierarchical HMM for Eye Movement Classification

• URL: http://arxiv.org/abs/2008.07961v1
• Date: Tue, 18 Aug 2020 14:47:23 GMT
• Title: Hierarchical HMM for Eye Movement Classification
• Authors: Ye Zhu, Yan Yan, and Oleg Komogortsev
• Abstract summary: We tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. We propose a hierarchical Hidden Markov Model (HMM) statistical algorithm for detecting fixations, saccades and smooth pursuits.
• Score: 14.551782298808572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. The efficient classification of these different types of eye movements helps to better analyze and utilize the eye tracking data. Different from the existing methods that detect eye movement by several pre-defined threshold values, we propose a hierarchical Hidden Markov Model (HMM) statistical algorithm for detecting fixations, saccades and smooth pursuits. The proposed algorithm leverages different features from the recorded raw eye tracking data with a hierarchical classification strategy, separating one type of eye movement each time. Experimental results demonstrate the effectiveness and robustness of the proposed method by achieving competitive or better performance compared to the state-of-the-art methods.

Related papers

• EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR [58.33693755009173]
  EyeSeg is an uncertainty-aware eye segmentation framework for augmented reality (AR) and virtual reality (VR)<n>We show that EyeSeg achieves segmentation improvements of MIoU, E1, F1, and ACC surpassing previous approaches.
  arXiv  Detail & Related papers  (2025-07-13T14:33:10Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• Active Gaze Control for Foveal Scene Exploration [124.11737060344052]
  We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene. The proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts.
  arXiv  Detail & Related papers  (2022-08-24T14:59:28Z)
• TraClets: Harnessing the power of computer vision for trajectory classification [0.9405458160620532]
  This research is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms.
  arXiv  Detail & Related papers  (2022-05-27T10:28:05Z)
• Individual Topology Structure of Eye Movement Trajectories [6.09170287691728]
  We propose an application of a new class of features to the quantitative analysis of personal eye movement trajectories structure. We experimentally demonstrate the competitiveness of the new class of features with the traditional ones and their significant synergy.
  arXiv  Detail & Related papers  (2022-05-21T20:30:45Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• Bayesian Eye Tracking [63.21413628808946]
  Model-based eye tracking is susceptible to eye feature detection errors. We propose a Bayesian framework for model-based eye tracking. Compared to state-of-the-art model-based and learning-based methods, the proposed framework demonstrates significant improvement in generalization capability.
  arXiv  Detail & Related papers  (2021-06-25T02:08:03Z)
• Classifying Eye-Tracking Data Using Saliency Maps [8.524684315458245]
  This paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data.
  arXiv  Detail & Related papers  (2020-10-24T15:18:07Z)
• Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization [2.5288257442251107]
  We propose an attribute normalization strategy based on deep learning generative frameworks. It reduces the variability of the samples used in pairwise comparisons, without reducing their discriminability. Our experiments were performed in two different datasets.
  arXiv  Detail & Related papers  (2020-02-10T17:55:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.