3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
Qinyu Chen (Universitat Zurich, ETH Zürich)
Zuowen Wang (Universitat Zurich, ETH Zürich)
Shih Chii Liu (Universitat Zurich, ETH Zürich)
Chang Gao (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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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× without losing accuracy when tested on a 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 https://github.com/qinche106/cb-convlstm-eyetracking.