Bidirectional Selective Recurrent Model for Accurate and Stable Event-based Eye Tracking

Основні поняття

A novel bidirectional and selective recurrent model, MambaPupil, is proposed to effectively leverage temporal context information for accurate and stable event-based eye tracking, outperforming state-of-the-art methods.

Анотація

The paper proposes the MambaPupil framework for event-based eye tracking, which addresses the key challenges posed by the diversity and abruptness of eye movement patterns.
The key highlights are:

MambaPupil utilizes a Dual Recurrent Module that combines a bidirectional Gated Recurrent Unit (Bi-GRU) and a Linear Time-Varying State Space Module (LTV-SSM) to extract temporal context information bidirectionally and selectively.

The Bi-GRU captures the complete contextual information through bidirectional temporal modeling, while the LTV-SSM achieves selective temporal state encoding by adjusting the importance of different eye movement stages.

The Bina-rep, a compact event representation, and the Event-Cutout data augmentation technique are employed to enhance the model's robustness against noise and external interference.

Evaluation on the challenging ThreeET-plus benchmark demonstrates the superior performance of MambaPupil, securing the 1st place in the CVPR'2024 AIS Event-based Eye Tracking challenge.

Статистика

Event cameras generate signals when brightness changes occur, and the event information produced when the eye is at rest is extremely sparse.
Blinking generates numerous irrelevant events, which may cause non-negligible fluctuations in the model's predictions in a short period.
Interference from other objects, such as glasses, eyelashes, and reflections on irises, can be detrimental to pupil tracking.

Цитати

"To fully extract and utilize the temporal correlation of pupil movement, this paper attempts to model the temporal relationship bidirectionally and selectively and proposes the MambaPupil model for stable eye tracking."
"To further improve the model's generalization performance, the Bina-rep, an event spatial-temporal binarization representation, is utilized as network input."
"A tailor-made data augmentation technique, Event-Cutout, is proposed to enhance the robustness of spatial localization by spatial random masking."

Ключові висновки, отримані з

MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking

by Zhong Wang,Z... о arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12083.pdf

Глибші Запити

How can the MambaPupil framework be extended to handle more complex eye movement patterns, such as those observed in individuals with neurological disorders or visual impairments

To extend the MambaPupil framework to handle more complex eye movement patterns observed in individuals with neurological disorders or visual impairments, several adaptations and enhancements can be implemented.

Incorporating Disease-Specific Data: Collecting and incorporating data specific to neurological disorders or visual impairments can help train the model to recognize and track the unique eye movement patterns associated with these conditions. This data can include variations in blinking patterns, saccades, smooth pursuits, and other movements relevant to the specific disorder.

Fine-Tuning for Specific Conditions: Fine-tuning the model on datasets that focus on neurological disorders or visual impairments can help the MambaPupil framework adapt to the nuances of these conditions. This process involves adjusting the model's parameters to better capture and predict the atypical eye movements seen in these populations.

Integrating Clinical Expertise: Collaborating with clinicians and experts in the field of neurology and ophthalmology can provide valuable insights into the distinct eye movement patterns associated with different disorders. Their input can guide the development of the model to accurately track and analyze these patterns.

Enhancing Robustness and Generalization: Implementing robustness techniques such as additional data augmentation methods tailored to specific disorders, introducing regularization techniques, and optimizing hyperparameters can enhance the model's ability to generalize across a diverse range of eye movement patterns.

Continuous Learning and Adaptation: Implementing a continuous learning framework that allows the model to adapt and improve over time based on new data and feedback from users with neurological disorders or visual impairments can ensure the model remains effective and up-to-date in tracking complex eye movements.

What other event-based sensing modalities could benefit from the bidirectional and selective temporal modeling approach used in MambaPupil, and how would the model need to be adapted

The bidirectional and selective temporal modeling approach used in the MambaPupil framework can benefit various other event-based sensing modalities by enhancing their ability to capture and analyze temporal correlations in asynchronous data streams. Some modalities that could benefit from this approach include:

Gesture Recognition: Applying the bidirectional and selective temporal modeling to event-based sensors used for gesture recognition can improve the accuracy and efficiency of recognizing complex hand movements and gestures in real-time.

Audio Event Detection: Utilizing the temporal modeling approach in event-based audio sensors can enhance the detection and classification of audio events, such as speech recognition, environmental sound analysis, and acoustic event detection.

Health Monitoring: Implementing the bidirectional temporal modeling in event-based sensors for health monitoring applications, such as heartbeat detection, gait analysis, and activity recognition, can provide more accurate and timely insights into an individual's health status.

Environmental Sensing: Integrating the selective temporal modeling approach in event-based sensors for environmental monitoring, such as air quality assessment, object tracking, and anomaly detection, can improve the detection and analysis of dynamic events in complex environments.

Adapting the MambaPupil model for these modalities would involve customizing the network architecture, data preprocessing techniques, and loss functions to suit the specific characteristics and requirements of each sensing domain.

Given the potential for event-based eye tracking in virtual and augmented reality applications, how could the MambaPupil model be integrated with other computer vision and interaction techniques to enable more immersive and responsive user experiences

Integrating the MambaPupil model with other computer vision and interaction techniques can enhance the user experience in virtual and augmented reality applications by enabling more immersive and responsive interactions. Here are some ways the model can be integrated:

Gaze-Based Interaction: Combining the MambaPupil model with gaze estimation algorithms can enable precise and intuitive gaze-based interaction in virtual and augmented reality environments. This integration can enhance user engagement and control in virtual worlds.

Object Recognition and Interaction: Integrating the MambaPupil model with object recognition algorithms can allow users to interact with virtual objects using eye movements. This can enable hands-free manipulation of virtual objects and enhance the realism of virtual experiences.

Emotion Recognition: By incorporating emotion recognition capabilities into the MambaPupil model, virtual and augmented reality applications can adapt to users' emotional states based on their eye movements. This can personalize the user experience and create more engaging virtual environments.

Enhanced Immersion: Leveraging the bidirectional and selective temporal modeling approach of MambaPupil, virtual and augmented reality applications can provide more immersive experiences by accurately tracking and responding to users' eye movements in real-time. This can enhance the sense of presence and realism in virtual environments.

By integrating the MambaPupil model with these techniques, virtual and augmented reality applications can offer more natural and intuitive interactions, leading to a more engaging and immersive user experience.