Method for Target Detection in a High Noise Environment Through Frequency Analysis Using an Event-Based Vision Sensor

Event-based vision sensors (EVSs), often referred to as neuromorphic cameras, operate by responding to changes in brightness on a pixel-by-pixel basis. In contrast, traditional framing cameras employ some fixed sampling interval where integrated intensity is read off the entire focal plane at once. Similar to traditional cameras, EVSs can suffer loss of sensitivity through scenes with high intensity and dynamic clutter, reducing the ability to see points of interest through traditional event processing means. This paper describes a method to reduce the negative impacts of these types of EVS clutter and enable more robust target detection through the use of individual pixel frequency analysis, background suppression, and statistical filtering. Additionally, issues found in normal frequency analysis such as phase differences between sources, aliasing, and spectral leakage are less relevant in this method. The statistical filtering simply determines what pixels have significant frequency content after the background suppression instead of focusing on the actual frequencies in the scene. Initial testing on simulated data demonstrates a proof of concept for this method, which reduces artificial scene noise and enables improved target detection.