Experimental methods to predict dynamic vision sensor event camera performance

Abstract

Dynamic vision sensors (DVS) represent a promising new technology, offering low power consumption, sparse output, high temporal resolution, and wide dynamic range. These features make DVS attractive for new research areas including scientific and space-based applications; however, more precise understanding of how sensor input maps to output under real-world constraints is needed. Often, metrics used to characterize DVS report baseline performance by measuring observable limits but fail to characterize the physical processes at the root of those limits. To address this limitation, we describe step-by-step procedures to measure three important performance parameters: (1) temporal contrast threshold, (2) cutoff frequency, and (3) refractory period. Each procedure draws inspiration from previous work, but links measurements sequentially to infer physical phenomena at the root of measured behavior. Results are reported over a range of brightness levels and user-defined biases. The threshold measurement technique is validated with test-pixel node voltages, and a first-order low-pass approximation of photoreceptor response is shown to predict event cutoff temporal frequency to within 9% accuracy. The proposed method generates lab-measured parameters compatible with the event camera simulator v2e, allowing more accurate generation of synthetic datasets for innovative applications.