3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network

• URL: http://arxiv.org/abs/2308.11771v1
• Date: Tue, 22 Aug 2023 20:24:24 GMT
• Title: 3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
• Authors: Qinyu Chen, Zuowen Wang, Shih-Chii Liu, Chang Gao
• Abstract summary: This paper presents a sparse Change-Based Congenerational Long ShortTerm Memory (CB-ConvLSTM) model for event-based eye tracking. We leverage the benefits of retina-inspired event cameras, their low-latency response and sparse output stream, over traditional frame-based cameras.
• Score: 12.697820427228573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technology such as AR/VR headsets. We leverage the benefits of retina-inspired event cameras, namely their low-latency response and sparse output event stream, over traditional frame-based cameras. Our CB-ConvLSTM architecture efficiently extracts spatio-temporal features for pupil tracking from the event stream, outperforming conventional CNN structures. Utilizing a delta-encoded recurrent path enhancing activation sparsity, CB-ConvLSTM reduces arithmetic operations by approximately 4.7$\times$ without losing accuracy when tested on a \texttt{v2e}-generated event dataset of labeled pupils. This increase in efficiency makes it ideal for real-time eye tracking in resource-constrained devices. The project code and dataset are openly available at \url{https://github.com/qinche106/cb-convlstm-eyetracking}.

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