Francisco Barranco

About

Francisco Barranco is a computer vision and robotics researcher whose work sits at the intersection of neuromorphic computing, autonomous navigation, and real-time visual processing. He has made particularly significant contributions to event-based vision — a biologically inspired sensing paradigm that mimics the asynchronous firing of retinal neurons — developing techniques that exploit the unique temporal and computational advantages these sensors offer over conventional cameras. Barranco's most influential contribution, "Real-Time Clustering and Multi-Target Tracking Using Event-Based Sensors" (2018), has accumulated 86 citations and established practical methods for harnessing neuromorphic sensors in demanding tracking and detection tasks. His early benchmark dataset for neuromorphic visual navigation (2016, 44 citations) helped standardize evaluation across the research community, accelerating broader adoption of event-driven methods. A 2024 review on neuromorphic perception for mobile robotics (25 citations) reflects his sustained leadership in this evolving field. Beyond neuromorphic vision, Barranco has contributed to hardware-accelerated visual attention systems using FPGAs, binocular disparity estimation for active robotic vision, and optical flow computation — demonstrating a consistent focus on making sophisticated visual algorithms viable for real-time, resource-constrained robotic platforms. His body of work represents a coherent research vision bridging biological inspiration with practical autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-Time Clustering and Multi-Target Tracking Using Event-Based Sensors

   86 citations · 2018
2. 2
   A Dataset for Visual Navigation with Neuromorphic Methods

   44 citations · 2016
3. 3
   Neuromorphic Perception and Navigation for Mobile Robots: A Review

   25 citations · 2024
4. 4
   Real-Time Visual Saliency Architecture for FPGA With Top-Down Attention Modulation

   18 citations · 2014
5. 5
   Vector Disparity Sensor with Vergence Control for Active Vision Systems

   6 citations · 2012
6. 6
   A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation

   4 citations · 2012
7. 7
   Event-based Vision for Early Prediction of Manipulation Actions

   3 citations · 2023
8. 8
   Real-time clustering and multi-target tracking using event-based sensors

   2 citations · 2018

Key Collaborators

Contact & Links