I do research

to save the life of our children

I wish to make driverless vehicles a reality by 2030~2050.

2013 Listed as CVPR 2013 outstanding reviewer

Handling occlusions with franken-classifiers

M. Mathias, R. Benenson, R. Timofte, L. Van Gool
ICCV 2013

@INPROCEEDINGS{Mathias2013Iccv,
  author = {M. Mathias and R. Benenson and R. Timofte and L. {Van Gool}},
  title = {Handling Occlusions with Franken-classifiers},
  booktitle = {ICCV},
  year = {2013}
}

Detecting partially occluded pedestrians is challenging.
Here we study why our classifier performs poorly under occlusion and we show that using a set of occlusion specific classifiers can significantly improve detection quality. Our proposed franken-classifiers obtain sub-linear grows in training and testing time, while keeping high detection quality.

The presented poster.


Seeking the strongest rigid detector

R. Benenson*, M. Mathias*, T. Tuytelaars, L. Van Gool (* indicates equal contribution.)
CVPR 2013
Chef's recommendation

@INPROCEEDINGS{Benenson2013Cvpr,
  author = {R. Benenson and M. Mathias and T. Tuytelaars and L. {Van Gool}},
  title = {Seeking the strongest rigid detector},
  booktitle = {CVPR},
  year = {2013}
}

Previous work to boost detection quality explored using non-linear SVMs, more sophisticated features, geometric priors, motion information, deformable models, or deeper architectures. We use none of these.
Using a single rigid classifier per candidate detection window we reach or improve state-of-the-art performance for pedestrian detection on INRIA, ETH and Caltech USA datasets.

Watch result video, and the presented poster.


Traffic Sign Recognition - How far are we from the solution?

M. Mathias*, R. Timofte*, R. Benenson, L. Van Gool (* indicates equal contribution.)
IJCNN 2013

@INPROCEEDINGS{Mathias2013Ijcnn,
  author = {M.  Mathias and R. Timofte and R. Benenson and L. {Van Gool}},
  title = {Traffic Sign Recognition - How far are we from the solution?},
  booktitle = {ICJNN},
  year = {2013}
}

We show that, without any application specific modification, existing methods for pedestrian detection and face recognition; can reach performances in the range of 95%∼99% of the perfect solution on current traffic sign datasets.

Watch result video.

This work was one of the winners of the german traffic sign detection benchmark, see details in related publication.


2012 First time at CVPR and ECCV. Obtains best CVVT workshop paper award

Fast stixels estimation for fast pedestrian detection

R. Benenson, M. Mathias, R. Timofte, L. Van Gool
ECCV 2012, CVVT workshop
Selected best paper of the workshop.

@INPROCEEDINGS{Benenson2012EccvCvvtWorkshop,
  author = {R. Benenson and M. Mathias and R. Timofte and L. {Van Gool}},
  title = {Fast stixels estimation for fast pedestrian detection},
  booktitle = {ECCV, CVVT workshop},
  year = {2012}
}

We revisit the stixel computation method.
Stixels computation now reaches 200 Hz (300 Hz latest version), and we can detect pedestrians at the record speed of 165 Hz (with state of the art quality).

Watch result video, and the presentation slides.

Source code available, with a research only license.


Stixels motion estimation without optical flow computation

B. Gunyel, R. Benenson, R. Timofte, L. Van Gool
ECCV 2012

@INPROCEEDINGS{Gunyel2012Eccv,
  author = {B. Gunyel and R. Benenson and R. Timofte and L. {Van Gool}},
  title = {Stixels motion estimation without optical flow computation},
  booktitle = {ECCV},
  year = {2012}
}

We estimate the motion of objects without computing optical flow.

Watch a short result video, and the presented poster.


Pedestrian detection at 100 frames per second

R. Benenson, M. Mathias, R. Timofte, L. Van Gool
Oral at CVPR 2012, 2.5% acceptance rate
Chef's recommendation

@INPROCEEDINGS{Benenson2012Cvpr,
  author = {R. Benenson and M. Mathias and R. Timofte and L. {Van Gool}},
  title = {Pedestrian detection at 100 frames per second},
  booktitle = {CVPR},
  year = {2012}
}

We propose a new detector that improves both speed and quality over state-of-the-art single part detectors. We reach 50 Hz in monocular setup, and 135 Hz when using stixels on a street scene (including the stereo processing time).

Download presentation slides. Watch CVPR talk.

Watch CVPR result video.

Watch Europa project result video, our detector running inside a robot, together with a tracker from RWTH Aachen.

Source code available, with a research only license.


2011 First time at ICCV

Stixels estimation without depth map computation

R. Benenson, R. Timofte, L. Van Gool
ICCV 2011, CVVT workshop

@INPROCEEDINGS{Benenson2011IccvWorkshop,
  author = {R. Benenson and R. Timofte and L. {Van Gool}},
  title = {Stixels estimation without depthmap computation},
  booktitle = {ICCV, CVVT workshop},
  year = {2011}
}

We detect (unknown) objects on a street scene faster than it takes to compute a depth map.

Source code available, with a research only license.


2010 First year at Visics, scratching my head

2009 Working on industrial applications

Working on motion planning software for industrial projects.


2008 Got my PhD!

Achievable safety of driverless ground vehicles

R. Benenson, T. Fraichard, M. Parent
ICARCV 2008
Chef's recommendation

@INPROCEEDINGS{Benenson2008Icarcv,
  author = {R. Benenson and T. Fraichard and M. Parent},
  title = {Achievable safety of driverless vehicles},
  booktitle = {ICARCV},
  year = {2008},
  url = {http://hal.inria.fr/inria-00294750}
}

Which safety guarantees can we give for autonomous vehicles?
Under which conditions these guarantees hold?
Which guarantees can we give during extraordinary situations?


Perception for driverless vehicles

R. Benenson
PhD thesis, Mines Paristech 2008

@PHDTHESIS{Benenson2008PhD,
  author = {Rodrigo Benenson},
  title = {Perception for driverless vehicles in urban environment},
  school = {Ecole des Mines de Paris},
  year = {2008},
  url = {http://pastel.paristech.org/5327}
}

How to design a driverless vehicle ?
Core contributions in the safety design, perception algorithm, and a proof-of-concept system integration.

Watch online


Design of an urban driverless ground vehicle

R. Benenson, M. Parent
IROS 2008
Chef's recommendation

@INPROCEEDINGS{Benenson2008Iros,
  author = {R. Benenson and M. Parent},
  title = {Design of a driverless vehicle for urban environment},
  booktitle = {IROS},
  year = {2008},
  url = {http://hal.inria.fr/inria-00295152}
}

Updated version of our IROS 2006 work.

Introduction of the harm function, integration with global planning system, (primitive) use of vision for traversability estimation.


Towards urban driverless vehicles

R. Benenson, S. Petti, T. Fraichard, M. Parent
International Journal of Vehicle Autonomous Systems 2008

@ARTICLE{Benenson2008Ijvas,
  author = {Rodrigo Benenson and St{\'e}phane Petti and Thierry Fraichard and Michel Parent},
  title = {Toward urban driverless vehicles},
  journal = {Int. Journal of Vehicle Autonomous Systems, Special Issue on Advances in Autonomous Vehicle Technologies for Urban Environment},
  year = {2008},
  volume = {1},
  pages = {4 - 23},
  number = {6},
  url = {http://hal.inria.fr/inria-00115112}
}

Journal version of our IROS 2006 work.


2007 First time at ICRA

Cybernetic transportation systems design and development: simulation software

S. Boissé, R. Benenson, L. Bouraoui, M. Parent, L. Vlacic
ICRA 2007

This paper describes the simulator we developed in order to validate our on-board software in single and multiple vehicles scenarios.


Networking needs and solutions for road vehicles at IMARA

O. Mehani, R. Benenson, S. Lemaignan, T. Ernst
ITST 2007

Which are the communications need for driverless vehicles?


2006 First time at IROS

Integrating perception and planning for autonomous navigation of urban vehicles

R. Benenson, S. Petti, M. Parent, T. Fraichard
IROS 2006

@INPROCEEDINGS{Benenson2006Iros,
  author = {Rodrigo Benenson and St{\'e}phane Petti and Michel Parent and Thierry Fraichard},
  title = {Integrating Perception and Planning for Autonomous Navigation of Urban Vehicles},
  booktitle = {IROS},
  year = {2006},
  url = {http://hal.inria.fr/inria-00086286}
}

Motion estimation, online static map building, moving objects detection and tracking, safe planning and control; all of it running in real-time on single PC, controlling an electric vehicle.

See the proof of concept video.


Real-time vehicle motion estimation using texture learning and monocular vision

Y. Dumortier, R. Benenson, M. Kais
ICCVG 2006

On how we do motion estimation using the ground texture.

2005 First year of PhD


Diseño e implementación de un receptor digital de radio

R. Benenson
Engineer thesis (in Spanish), UTFSM 2004


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Due to industrial partnerships, most of the code I write is closed source.
Happily, I have been recently authorized to release the source code of my work on fast pestrians detection.

Other than writing my own original research code, I have also ported a couple of algorithms developed by other authors:

Otherwise, here is a list of interesting open-source code I have used:

Are we there yet ?

Did you ever want to quickly learn which paper provides the best results on standard dataset X ?

I have created a mini-site to aggregate in one place results on key computer vision datasets.
The results set is crowd sourced. You are welcome to provide inputs and comments.

Below, some of the datasets I have used during my research:

My name is Rodrigo Benenson.

I studied electronics engineering at UTFSM.

I did my PhD in robotics inside the Lara team, part of Mines Paristech and INRIA.

I then moved for a post-doc in computer vision at Visics, KU Leuven.

I currently continue my research at the Max-Planck-Institut für Informatik, in Saarbrücken, Germany.

You can contact me by sending an email at rodrigoDOTbenensonATgmailDOTcom.



想建设一个更美好的世界。


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