# High-definition event frame generation using SoC FPGA devices

arXiv: https://arxiv.org/abs/2307.14177 · HTML: https://mlsanity.com/paper/2307.14177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 26 Jul 2023.

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

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Temporal Binary Representation for Event-Based Action Recognition](https://mlsanity.com/paper/2010.08946/) (2020)
- [Event-based Feature Extraction Using Adaptive Selection Thresholds](https://mlsanity.com/paper/1907.07853/) (2019)
- [Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation](https://mlsanity.com/paper/1706.05415/) (2017)
- [Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification](https://mlsanity.com/paper/1905.07419/) (2019)
- [Traffic Sign Detection With Event Cameras and DCNN](https://mlsanity.com/paper/2207.13345/) (2022)
- [Fast-moving object counting with an event camera](https://mlsanity.com/paper/2212.08384/) (2022)

## Built on by (most cited first, 2 of 2)

- [Event-based vision on FPGAs -- a survey](https://mlsanity.com/paper/2407.08356/) (2024)
- [HOMI: Ultra-Fast EdgeAI platform for Event Cameras](https://mlsanity.com/paper/2508.12637/) (2025)

## Abstract (arXiv)

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD -1280 x 720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods.