# Autobiasing Event Cameras

arXiv: https://arxiv.org/abs/2411.00729 · HTML: https://mlsanity.com/paper/2411.00729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial](https://mlsanity.com/paper/1412.4031/) (2014)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [Unraveling the paradox of intensity-dependent DVS pixel noise](https://mlsanity.com/paper/2109.08640/) (2021)
- [Optimal biasing and physical limits of DVS event noise](https://mlsanity.com/paper/2304.04019/) (2023)
- [Control and Evaluation of Event Cameras Output Sharpness via Bias](https://mlsanity.com/paper/2210.13929/) (2022)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Autofocus for Event Cameras](https://mlsanity.com/paper/2203.12321/) (2022)

## Built on by (most cited first, 3 of 3)

- [Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis](https://mlsanity.com/paper/2603.08386/) (2026)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)
- [Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis](https://mlsanity.com/paper/2610.06973/) (2026)

## Abstract (arXiv)

This paper presents an autonomous method to address challenges arising from severe lighting conditions in machine vision applications that use event cameras. To manage these conditions, the research explores the built in potential of these cameras to adjust pixel functionality, named bias settings. As cars are driven at various times and locations, shifts in lighting conditions are unavoidable. Consequently, this paper utilizes the neuromorphic YOLO-based face tracking module of a driver monitoring system as the event-based application to study. The proposed method uses numerical metrics to continuously monitor the performance of the event-based application in real-time. When the application malfunctions, the system detects this through a drop in the metrics and automatically adjusts the event cameras bias values. The Nelder-Mead simplex algorithm is employed to optimize this adjustment, with finetuning continuing until performance returns to a satisfactory level. The advantage of bias optimization lies in its ability to handle conditions such as flickering or darkness without requiring additional hardware or software. To demonstrate the capabilities of the proposed system, it was tested under conditions where detecting human faces with default bias values was impossible. These severe conditions were simulated using dim ambient light and various flickering frequencies. Following the automatic and dynamic process of bias modification, the metrics for face detection significantly improved under all conditions. Autobiasing resulted in an increase in the YOLO confidence indicators by more than 33 percent for object detection and 37 percent for face detection highlighting the effectiveness of the proposed method.