Publication: 3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
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Chen, Q., Wang, Z., Liu, S.-C., & Gao, C. (2023). 3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network. 1–5. https://doi.org/10.1109/biocas58349.2023.10389062
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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
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Chen, Q., Wang, Z., Liu, S.-C., & Gao, C. (2023). 3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network. 1–5. https://doi.org/10.1109/biocas58349.2023.10389062
