Abstract：The problems of dynamic blur and low temporal resolution in capturing eye movements with traditional cameras are addressed by employing an event camera for close-range capture and constructing a spiking-eye dataset. A spiking neural network model with a coordinate attention referred to as CA-SpikingRepVGG. The model reads encoded event data and performs feature extraction using the attention-based backbone network, followed by detection using the detection head. Experimental results demonstrate that CA-SpikingRepVGG achieves a mean average precision RP of 70.8%. Compared to SpikingVGG-16, the model shows a 15.9% improvement in RP and a 14.2% increase in Rr. With only one-third of the training time required by SpikingDensenet, the model achieves a 1.8% improvement in RP and a 0.9% improvement in Rr. These results indicate that the proposed model exhibits stronger eye detection and tracking capabilities in the context of eye movement, effectively accomplishing gaze estimation tasks.
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2024, Vol. 45 
