Event Camera Tuning for Detection Applications

Abstract

One of the main challenges in unlocking the potential of neuromorphic cameras, also called ''event camera'', is the development of novel methods that solve the multi-variable problem of adjusting their biases parameters to accommodate a desired task. Actually, it is very difficult to find in the literature a systematic heuristic that solves the problem for any desired application. In this paper we present a tuning parameters heuristic for the biases of event cameras, for tasks that require small objects detection in staring scenarios. The main purpose of the heuristic is to squeeze the camera's potential, optimize its performance, and expand its detection capabilities as much as possible. In the presentation, we translate the experimental properties of event camera and systemic constrains into mathematical terms, and show, under certain assumptions and classical tools from functional analysis, how the multi-variable problem collapses into a two-parameter problem that can be solved experimentally. A main conclusion that will be demonstrated is that for certain desired signals, such as the one provided by an incandescent lamp powered by the periodic electrical grid, the optimal values of the camera are very far from the default values recommended by the manufacturer.