VIRAT 2008
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Team 1: Kitware
Participants:
- Anthony Hoogs (Kitware) - coordinates overall
- Trevor Darrell (ICSI, UC Berkeley) - Coordinates UCBerkeley (Darrell, Malik) and Caltech
- Honeywell, General Dynamics, CMU, Maryland, U. Texas ...
Budget: 75K$ per year, 18 + 12 + 12 months
Team 2: BAE
Participants:
- Joel Douglas, Director, Computer Vision Group, BAE
- Trevor Darrell (ICSI, UC Berkeley) - Coordinates UCBerkeley (Darrell, Malik) and Caltech
Budget: 75K$ per year, 18 + 12 + 12 months
Proposing: Crowd-level activity recognition.
Team 3: Northrop Grumman
Participants:
- Paul Smith, Principal Investigator, Northrop Grumman
- Dave Newburn, Northrop Grummann - Proposal Manager
Utopia Compression
- Hai Wei, Utopia Compression- Main POC. (he will integrate Caltech's proposal with rest)
- Joseph Yadegar and Emily Murdockn - NDA, budgets, signature, etc.
- Hieu Tat Nguyen - Technical lead
hieu@utopiacompression.com Tel: 310-473-1500 x.105)
Budget:??
- Shooting for 1 post-doc + odds and ends = 130K$
Notes: Apr 22 08 |
General Info
- Name: Pietro Perona
- Organization: California Institute of Technology (Caltech)
- Degree: PhD, 1990, University of California at Berkeley
- Title: Allen E. Puckett Professor and Professor of Electrical Engineering
- Other: Executive Officer (Chair), Computation and Neural Systems, Caltech
- Prior work relevant to Virat (venue of representative publication in brackets):
- Representation of visual categories (FG05, CVPR96, CVPR98)
- Learning visual categories with weak or no supervision (ECCV00, CVPR03)
- One-shot learning of visual categories (ICCV03)
- Constellation models for human motion classification (ECCV00, PAMI03)
- Learning movemes without supervision (CVPR05, C. Fanti's PhD thesis, 08)
- Model of visual saliency (NIPS06)
- Interplay of generative and discriminative models for recognition (IJCV08)
- Narrative on prior work: Visual `events' or `objects' (actions, activities, objects, scenes) are naturally grouped by humans into categories. In order for machine to categorize visual events we need to address three issues: (a) how to model categories, (b) how to match models to images (detection, classification), (c) how to learn models from training examples. Professor Perona's research centers on these three issues, with a special focus on the learning problem.
- Facilities: Professor Perona's laboratory occupies approximately 2000sqft in the Moore Building at Caltech in Pasadena. The laboratory includes office space for the PI, an assistant and 12 post-docs and students, as well as a meeting area, a psychophysics room and a hardware work area. Equipment includes 18 high-end workstations, a computational cluster with 30 CPUs, 4Tbytes disk space, a 50Hz human motion capture system with 6 cameras and IR lighting fixtures.
- Most related previous projects:
- 2000-05 - MURI on recognition of actions and activities (subcontract from UC Berkeley)
- 2005-10 - MURI ``Learning to Recognize for Visual Surveillance (Caltech is prime, other sites: UCLA, MIT, UCBerkeley, UCIrvine, U. Illinois)