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Grace Vesom, 2016-06-20 06:47 am
OpenCVâs Peopleâs Choice Best Papers and State of the Art Vision Challenge Winners¶
Peopleâs choice awards for best papers from CVPR 2015 and winning algorithms of the Vision Challenge.
This is a short workshop, one hour before lunch, to announce and describe winners of two separate contests:
Location: Room 101 (~123) Time: 11am-12pm
Peopleâs Choice: Best paper¶
We will tally the peopleâs vote for best paper/paper youâd most like to see implemented. Weâll present the histogram of results which is an indication of the algorithms people are interested in overall and then list the 5 top winners.
Prizes will be awarded in two stages:- A modest award for winning and
- a larger award for presenting the code w/in 5 months as a pull request to OpenCV as Detailed here:
- Win: $500; Submit code: $6000
- Win: $300; Submit code: $4000
- Win: $100; Submit code: $3000
- Win: $50; Submit code: $3000
- Win: $50; Submit code: $3000
- (user) http://opencv.org/ and
- (developer) http://code.opencv.org/projects/opencv/wiki
State of the Art Vision Challenge¶
Our aim is to make available state of the art vision in OpenCV. We thus ran a vision challenge to meet or exceed the state of the art in various areas. We will present the results. The contest details are available at:
http://code.opencv.org/projects/opencv/wiki/VisionChallenge
- Win: $1000; Submit code: $3000
- Win: $1000; Submit code: $3000
- Win: $1000; Submit code: $3000
- Win: $1000; Submit code: $3000
- Win: $1000; Submit code: $3000
In this contest, if someone does not submit the code, the unclaimed money may be reallocated to those who do at the sole discretion of the prize committee.
Proposers¶
- Dr. Gary Rost Bradski, VP Perception and Core Software at Magic Leap, Inc.
- Vadim Pisarevsky, Principal Engineer at Itseez
- Vincent Rabaud, Perception Team Manager at Aldebaran Robotics
- Grace Vesom, 3D Vision Senior Engineer at Magic Leap, Inc.
Presenters¶
Dr. Gary Rost Bradski is Chief Scientist of Computer Vision at Magic Leap. Gary founded OpenCV at Intel Research in 2000 and is currently CEO of nonprofit OpenCV.org. He ran the vision team for Stanley, the autonomous vehicle that completed and won the $2M DARPA Grand Challenge robot race across the desert. Dr. Bradski helped start up NeuroScan (sold to Marmon), Video Surf (sold to Microsoft), and Willow Garage (absorbed into Suitable Tech). In 2012, he founded Industrial Perception (sold to Google, August 2013). Gary has more than 100 publications and more than 30 patents and is co-author of a bestseller in its category Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Press.
Vadim Pisarevsky is the chief architect of OpenCV. He graduated from NNSU Cybernetics Department in 1998 with a Masterâs degree in Applied Math. Afterwards, Vadim worked as software engineer and the team leader of OpenCV project at Intel Corp in 2000-2008. Since May 2008 he is an employee of Itseez corp and now works full time on OpenCV under a Magic Leap contract.
Vincent Rabaud is the perception team manager at Aldebaran Robotics. He co-founded the non-profit OpenCV.org with Gary Bradski in 2012 while a research engineer at Willow Garage. His research interests include 3D processing, object recognition and anything that involves underusing CPUs by feeding them fast algorithms. Dr. Rabaud completed his PhD at UCSD, advised by Serge Belongie. He also holds a MS in space mechanics and space imagery from SUPAERO and a MS in optimization from the Ecole Polytechnique.
Grace Vesom is a senior engineer in 3D vision at Magic Leap and Director of Development for the OpenCV Foundation. Previously, she was a research scientist at Lawrence Livermore National Laboratory working on global security applications and completed her DPhil at the University of Oxford in 2010.
Details of the State of the Art Vision Challenge¶
OpenCV is launching a community-wide challenge to update and extend the OpenCV library with state of the art algorithms. An award pool of $20,000 will be provided to the best performing algorithms in the following 11 CV application areas:
- image segmentation
- image registration
- human pose estimation
- SLAM
- multi-view stereo matching
- object recognition
- face recognition
- gesture recognition
- action recognition
- text recognition
- tracking
We prepared code to read from existing data sets in each of these areas: modules/datasets
Conditions:¶
The OpenCV Vision Challenge Committee will judge up to five best entries.
- You may submit a new algorithm developed by yourself.
- You may submit an existing algorithm whether or not developed by yourself (as long as you own or re-implement it yourself).
- Up to 5 winning algorithms will receive $1000 each.
- For an additional $3000 to $15,000*, you must submit your winning code as an OpenCV pull request under a BSD or compatible license.
- You acknowledge that your code may be included, with citation, in OpenCV.
You may explicitly enter code for any work you have submitted to CVPR 2015 or its workshops. We will not unveil it until after CVPR.
Winners and prizes are at the sole discretion of the committee.
List of selected datasets and other details described here: OpenCV Vision Challenge
* We will have a professional programmer assist people with their pull requests. The final amount will be adusted by number of pull requests. The minimum will be $3000 additional dollars for a pull request. The prize committee may adjust the amounts upwards depending on remaining budget at the commitees sole discretion
Timeline:¶
Submission Period:
Now - May 15th 2015
Winners Announcement:
June 8th 2015 at CVPR 2015
Contact:¶
Q&A:¶
Q.: What should be in performance evaluation report? Shall we send any report or paper along with the code?
A.: Participants are required to send source code and a performance evaluation report of their algorithms. Report should be in the standard form of a paper with algorithm description. Evaluation should be performed on at least one of the chosen benchmark datasets associated with the building block. Evaluation methodology should be the same as specified by author of each dataset, this includes using the same train\validation\test splits, evaluation metrics, etc. In additional, we ask to report running time of algorithm and platform details to help with their comparison. Algorithm's accuracy should be compared with state-of-the-art algorithms. In addition, itâll be useful to compare it with algorithms implemented in OpenCV whenever possible. Source code and supplied documentation should contain clear description on how to reproduce evaluation results. Source code have to be compiled and run under Ubuntu 14.
Q.: Can I participate in this Vision Challenge by addressing building blocks different from the current 11 categories?
A.: For this Vision Challenge, we have selected 11 categories and 21 supporting datasets. To participate in the Vision Challenge you need to address at least one of the building blocks we have selected and get results in at least one of the chosen associated datasets. Results on additional datasets (e.g., depth channel) will be evaluated accordingly by the awarding committee.
This may be just the first one of a series of challenges and we want to hear from the vision community which building blocks should come next, for the possible next challenges. Please, send your suggestions to our e-mail: [email protected].
- Background Subtraction - 1 vote
- Point Cloud Registration - 1 vote
- Pedestrian Detection - 1 vote
- Text Recognition for Arabic language - 1 vote
Q.: Which external algorithms or libraries can we use?
A.: All used 3rd party code should have Permissive free software licence. The most popular such licenses are: BSD, Apache 2.0, MIT.
Q.: I don't find the tracking dataset loading in opencv_contrib/modules/datasets module.
A.: We are not implemented loading-evaluation code for VOT tracking dataset, because it already has its own toolkit.
Q.: Where I can find the Dataset Benchmarks?
A.: They are placed with samples in modules/datasets/samples.
