HashHeat: An O(C) Complexity Hashing-based Filter for Dynamic Vision Sensor

Abstract

Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events which are unwanted. We propose HashHeat, a hashing-based BA filter with O(C) complexity. It is the first spatiotemporal filter that doesn't scale with the DVS output size N and doesn't store the 32-bits timestamps. HashHeat consumes 100x less memory and increases the signal to noise ratio by 15x compared to previous designs.