# Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation

arXiv: https://arxiv.org/abs/1706.05415 · HTML: https://mlsanity.com/paper/1706.05415/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [Event-based vision on FPGAs -- a survey](https://mlsanity.com/paper/2407.08356/) (2024)
- [EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation](https://mlsanity.com/paper/2506.03512/) (2025)
- [Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation](https://mlsanity.com/paper/2305.13055/) (2023)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [High-definition event frame generation using SoC FPGA devices](https://mlsanity.com/paper/2307.14177/) (2023)
- [Flow-Based Visual Stream Compression for Event Cameras](https://mlsanity.com/paper/2403.08086/) (2024)
- [PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection with Event Data](https://mlsanity.com/paper/2407.08272/) (2024)
- [Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision](https://mlsanity.com/paper/2410.00368/) (2024)
- [hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow](https://mlsanity.com/paper/2112.06772/) (2021)
- [Robust Optical Flow Computation: A Higher-Order Differential Approach](https://mlsanity.com/paper/2410.09563/) (2024)
- [EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation](https://mlsanity.com/paper/2605.28312/) (2026)
- [Optical Flow Sensor: A Direction-Selective Bionic Retina Design](https://mlsanity.com/paper/2607.28686/) (2026)

## Abstract (arXiv)

Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30% compared with previous methods. The OF can be calculated on FPGA with 50,MHz clock in 0.2,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.