Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data
Unlocking efficient pedestrian detection without the manual labeling bottleneck
Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN detectors are trained with supervised backpropagation and therefore require costly frame-level crossing labels. We investigate cro...
ArXiv CS.CV
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This research breaks the bottleneck of costly human-labeled data by enabling event-based sensors to learn visual features through unsupervised spiking neural networks. Bridging the gap between unlabeled processing and supervised accuracy significantly lowers the barrier for deploying high-speed, power-efficient computer vision in autonomous robotics.
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