Multi-stage silicon retina on SCAMP-5 achieves 13% lower saliency prediction loss and 47% fewer events than standard DVS using a ~100k-parameter network.
Continuous-time Intensity Estimation Using Event Cameras
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abstract
Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods.
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Programmable Silicon Retina on Pixel Processor Array
Multi-stage silicon retina on SCAMP-5 achieves 13% lower saliency prediction loss and 47% fewer events than standard DVS using a ~100k-parameter network.