Sean Fanello

About

Sean Fanello is a leading researcher in computer vision and robotics, with a focus on real-time action recognition, 3D stereo estimation, and human-robot interaction. His most-cited work, "Keep It Simple and Sparse: Real-Time Action Recognition" (2017, 81 citations), introduced a streamlined approach for efficient, real-time gesture and action recognition, significantly advancing the field's practical applications. Fanello's contributions extend to robotic vision, where he developed a complete 3D stereo pipeline for humanoid robots, enabling online eye calibration and novel eye-hand coordination formulations (2014, 32 citations). He also pioneered the "iCub World" dataset (2013, 30 citations), a groundbreaking resource for object recognition acquired through human-robot interaction, facilitating rapid data annotation with ground truth. His work on weakly supervised strategies for natural object recognition (2013, 23 citations) and heteroscedastic independent motion detection (2012, 16 citations) further demonstrates his impact on robotics vision systems. With a total of over 200 citations across his top papers, Fanello's research has been instrumental in bridging computer vision and robotics, making real-time, autonomous systems more robust and practical. His achievements include advancing humanoid robot capabilities in gesture recognition and imitation learning, solidifying his reputation as a key innovator in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Keep It Simple and Sparse: Real-Time Action Recognition

   81 citations · 2017
2. 2
   3D stereo estimation and fully automated learning of eye-hand coordination in humanoid robots

   32 citations · 2014
3. 3
   iCub World: Friendly Robots Help Building Good Vision Data-Sets

   30 citations · 2013
4. 4
   Weakly supervised strategies for natural object recognition in robotics

   23 citations · 2013
5. 5
   A heteroscedastic approach to independent motion detection for actuated visual sensors

   16 citations · 2012
6. 6
   Ask the Image: Supervised Pooling to Preserve Feature Locality

   16 citations · 2014
7. 7
   On the impact of learning hierarchical representations for visual recognition in robotics

   15 citations · 2013
8. 8
   Visual recognition for humanoid robots

   11 citations · 2016
9. 9
   All gestures you can: A memory game against a humanoid robot

   8 citations · 2012
10. 10
    Arm-Hand Behaviours Modelling: From Attention to Imitation

    7 citations · 2010

Key Collaborators

Contact & Links