A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera
<|reference_start|>A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera: Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three ways: 1) deliberately targets a simple architecture and set of operations (convolutions, ReLU activations) 2) can be configured to perform online inference efficiently via buffering of layer outputs 3) can achieve more than 90% activation sparsity through regularization during training, enabling very significant efficiency gains on event-based processors. In addition, we propose a general affine augmentation strategy acting directly on the events, which alleviates the problem of dataset scarcity for event-based systems. We apply our model on the AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10 accuracy on the Kaggle private testset.<|reference_end|>
@article{pei2024a,
    title={A Lightweight Spatiotemporal Network for Online Eye Tracking with Event
  Camera},
    author={Yan Ru Pei, Sasskia Br"uers, S'ebastien Crouzet, Douglas McLelland,
  Olivier Coenen},
    journal={Proceedings of the IEEE/CVF Conference on Computer Vision and
  Pattern Recognition (CVPR) Workshops, 2024, pp. 5780-5788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08858},
    primaryClass={cs.CV cs.AI}
}