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A research strategic plan for computer vision at NICTA. Market Drivers. Safety and Security: SAFE: est video surveillance software in US – $1.8-4 billion Intelligent Transportation Systems: STARSense: SCATS installed in 1300 cities in 20 countries - PowerPoint PPT Presentation
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A research strategic plan for computer vision at NICTA
Market Drivers
• Safety and Security:– SAFE: est video surveillance software in US – $1.8-4
billion
• Intelligent Transportation Systems:– STARSense:
• SCATS installed in 1300 cities in 20 countries• More than 9 million vehicle trips daily in Sydney Urban
corridor• Aiming for 5% improvement in traffic flow• Dramatic decrease in cost of traffic sensing deployment
– Automap• Map development cost for TeleAtlas and Navteq est > US$1
billion• Potential price for fresh data for Aust capitals is $10m p/a
Market Drivers (cont)
• Biomedical and Life Sciences– Bionic eye and low vision devices:
• 3,300-4,000 people / million legally blind (AMD, retinopathy, cataract)
• 480,000 people in Australia with visual impairment• Cost of cochlea implant in US > US$60,000
• Environmental Management– GREEnMan:
• Agricultural industries in Australia approximately 135,000 farms
• 65,000 crop or horticultural• Plant industries value $15 billion at farm gate – contribution
to the economy of $10 billion
Problems
Problems
Problems
Problems
Research Approach
• Fusing key theory from– Multi-view Geometry– statistical pattern recognition
• Into key algorithms such as:– Detection– Tracking– Recognition
• To introduce novel theory driven approaches, that provide robust solutions to complex dynamic scene analysis
• Delivered as a NICTA-wide platform for computer vision
Approaches – Building in geometry: Night-time Traffic Surveillance• Objectives
– Count the numbers of vehicles for each lane
– Estimate the speed of the vehicles
– Classify the vehicles as car / truck or bus
– Detect the changes of lane– Make the installation easy,
automatic– Avoid the manual thresholds
(scene dependent)
Approaches – Bionic eye – a running example
Camera+processor Wireless link Implant
Approaches – Bionic eye – a running example
VIsion processing for the Bionic Eye
Signals on electrodes
Dynamic scene understanding:Real-time structure and motion recovery
object identification
Reprojection to RGC array
Approaches – Bionic eye (cont)
• Based on visual processing, context, motion and user feedback, we may choose what information to present at each retinal ganglion cell in each cycle.
Approaches – Bionic eye (cont)
• Centre for Eye Research Australia clinicians running focus groups with low vision subjects (Results June)
• Identified ‘friend recogniser’ as possible demonstrator– From wearable computing– Identify 20 close ‘friends’ in close proximity– Output via audio
• Problem: from wearable cameras/computers– Pedestrian detection/tracking– Face detection/tracking– Face recognition from small database
Approaches – Hardware enhanced detection
Towards Safer Roads by Integration of Road Scene Monitoring and Vehicle Control, L Petersson, L Fletcher, et. al. The International Journal of Robotics Research, 2006
Approaches – pedestrian detection
Sakrapee Paisitkriangkrai, Chunhua Shen, and Jian Zhang. An experimental evaluation of local features for pedestrian classification in DICTA'07, Dec 2007. IEEE Computer Society [Best Paper Award].
Approaches – fusing statistical approaches to tracking
Kernel-based tracking from a probabilistic viewpoint, Quang Nguyen, Antonio Robles-Kelly, Chunhua Shen, IEEE CVPR'07. Minnesota, USA, June, 2007.
Face detection and recognition
Approaches – Multi-body multi-motion recovery
Hongdong Li, Two-view Motion Segmentation from Linear Programming Relaxation, in Proceedings of CVPR, 2007
Approaches – Spherical optical flow time to collision – real-time motion understanding
C.MacCarthy, N. Barnes and R. Mahony, “A Robust Docking Strategy for a Mobile Robot using Flow Field Divergence”, IEEE Trans Robotics, in press 2008.C. McMcarthy, N. Barnes and M. Srinivasan"Real Time Biologically-Inspired Depth Maps from Spherical Flow", Proc. IEEE ICRA2007, Rome, Italy, May, 2007.
Approaches – Spherical egomotion recovery – statistical Geometry, for robust parallel implementation
John Lim and Nick Barnes, “Directions of Egomotion from Antipodal Points”, accepted, IEEE-CVPR, 2008
Approaches and complexities
NICTA-wide computer vision platform
• Parallel hardware:– Nvidia CUDA GPU– Blackfin DSP– Spartan 3 FPGA– Regular Intel SIMD multicore
• Software– implementations on NICTA Intel PXA270 Microprocessor-
based embedded platform
NICTA-wide platform project
Projects to be proposed
• VIBE (Vision Processing for the Bionic Eye)• Vision platform:
– Dynamic Scene understanding– Parallel embedded platform QRL + CRL– Serial embedded platform– Code contributions from all Labs– Focus on Detection/Tracking/Recognition
• GREEnMan
Why CV@NICTA is the best group to do this
• One of the strongest CV research groups in the world in terms of A+ and A publications at recent CV conferences (see appendices)
• One of the strongest CV research groups in the world in terms of citations (see appendices)
• One of the strongest groups in real-time structure from motion (see appendices)
• Dominant world group in Multi-body structure from motion
• Strong mix across research approaches in CV and pattern recognition
• World-class parallel real-time implementations (PMS)• World-class traffic surveillance systems• Outstanding linkages in safety and security
Contributors and acknowledgements
• Contributors– Nick Barnes, Abbas, Bigdeli, Terry Caelli, Richard Hartley,
Bernhard Hengst, Brian Lovell, Chris, Nicol, John Parker, Lars Petersson, Antonio Robles-Kelly, Jian Zhang
• Discussions with the following people greatly contributed to this plan:– Emma Barron, David Everitt, Terry Percival, Phil
Robertson, David Skellern, Chris Scott, Bob Williamson