next generation 4-d distributed modeling and visualization of battlefield
DESCRIPTION
Next Generation 4-D Distributed Modeling and Visualization of Battlefield. Avideh Zakhor UC Berkeley September 2004. Participants. Avideh Zakhor, (UC Berkeley) Bill Ribarsky, (Georgia Tech) Ulrich Neumann (USC) Pramod Varshney (Syracuse) Suresh Lodha (UC Santa Cruz). - PowerPoint PPT PresentationTRANSCRIPT
Next Generation 4-D Next Generation 4-D Distributed Modeling and Distributed Modeling and Visualization of BattlefieldVisualization of Battlefield
Avideh ZakhorUC Berkeley
September 2004
Participants
Avideh Zakhor, (UC Berkeley) Bill Ribarsky, (Georgia Tech) Ulrich Neumann (USC) Pramod Varshney (Syracuse) Suresh Lodha (UC Santa Cruz)
Battlefield Visualization
Detailed, timely and accurate picture of the modern battlefield vital to militaryMany sources of info to build “picture”:
Archival data, roadmaps, GIS and databases: static Sensor information from mobile agents at different times and locations Scene itself time varying; moving objects Multiple modalities: fusion
How to make sense of all these without information overload?
Visualization Pentagon
DecisionMaking underUncertainty
DecisionMaking underUncertainty
UncertaintyProcessing/
Visualization
UncertaintyProcessing/
Visualization
4D Modeling/Update
4D Modeling/Update
Visualizationand renderingVisualizationand rendering
Tracking/Registration
Tracking/Registration
Research Agenda for 2003- 2004
Modeling Visualization and Rendering
Mobile situational visualization Augmented virtual environments
Add the temporal dimension (4D): Tracking of moving objects in scenes Modeling of time varying objects and scenes Dynamic event analysis, and recognition
Path planning under uncertainty
Acquisition set up for dynamic scene modeling
rotating mirror
IR line laser
Digital camcorder with IR-filter
Halogen lamp with IR-filter
VIS-light camera
PC
Sync electronic
Reference object for H-line
Roast with vertical slices
Video intensity and IR captured synchronously
IR video stream VIS video stream
Frame rate: 30 Hz (NTSC) Frame rate: 10 Hz Synchronized with IR video
stream
Processing steps
•Compute depth at the horizontal line•Track computed depth values along vertical lines•Intraframe and interframe tracking•Dense depth estimation
Video analysis
− Segmenting and tracking moving objects (people, vehicles) in the scene
− Determines regions of interest/change and allows for dynamic modeling and rapid modeling
Dynamic Event Analysis
Video Scene Analysis: Activity Classification with Uncertainty
Example activities: sitting, bending and standing
The blue pointer indicates the level of certainty in the classifier decision
a
b
c
d
Audio Enhanced Visual Processing with Uncertainty
Video Processing and Classification
Audio Processingand Classification
Visualization
Description Generation
Video Acquisition
Sound Acquisition
FusionUncertainty
VE: captures only a snapshot of the real world, therefore lacks any representation of dynamic events and activities occurring in the scene
AVE Approach: uses sensor models and 3D models of the scene to integrate dynamic video/image data from different sources
AVE: Fusion of 2D Video & 3D Model
Visualize all data in a single context to maximize collaboration and comprehension of the big-picture
Address dynamic visualization and change detection
Mobile Situational Visualization System
Drawing Area
Buttons Pen Tool
Mobile Team
Collaboration Example
collaborators
Shared observations of vehicle location, direction, speed
Goal
Source
Optimal route planning for battlefield risk minimization
High risk
Moderate risk
Low risk
Risk free
Lidar Data Classification
Using height and height variation
Using LiDAR data (no aerial image)
Using all five features
Adaptive Stereo/Lidar based registration for modeling outdoor scenes
Aerial viewStereo Based Registration
LiDAR Based Registration
•LiDAR based approach seems better at turns.
•Stereo based approach captures terrain undulations
Punctuated Model Simplification• Our initial implementation considers planar loops.• The mesh containing the loops is a topological 2-manifold.
Example: simple object
Detected loops
“Inside/outside” binary tree
Simplification path
Interactions on AVE
Collaboration with Northrop Grumman- install v.1 AVE system (8/03) for demonstrations- Install v.2 AVE system (9/04) for demonstrations and
evaluation license Tech transfer
- Source code for LiDAR modeling to ARMY TEC labs- Integration into ICT training applications for MOUT after-
action review Demos/proposals/talks
− NIMA, NRO, ICT, Northrup Grumman , Lockheed Martin, HRL/DARPA, Olympus, Airborne1, Boeing
Transitions for 3D modeling• Carried out a 2 day modeling of Potomac Yard Mall in Washington, DC in December 2003 for Night Army Vision Lab, and GSTI
•Shipped equipment ahead of time
•Spent one day driving around acquiring data
•Spent ½ day processing the data
•Delivered the model to Jeff Turner of GSTI/ Night army vision lab
•Carried out another 2 day modeling of Ft. McKenna in Geogia in December 2003 in collaboration with Jeff Dehart of the ARL
•Drove the equipment from DC to Georgia in a van
•Collected data in one day, processed in few days
•Delivered the 3D model to Larry Tokarcik’s group.
•In Discussion with Harris to transition 3D modeling Architecure/software/hardware
•Invited talk at the registration workshop at CVPR
Technology Transfer on Sitvis
•We are continuing work centered around the mobile augmented battlefield visualization testbed with both the Georgia Tech and UNC Charlotte homeland security initiatives.
•Dr. Ribarsky is on the panel to develop the research agenda for the new National Visual Analytics Center, sponsored by DHS. Mobile situational visualization will be part of this agenda.
•The system is being used as part of the Sarnoff Raptor system, which is deployed to the Army and other military entities. In addition our visualization system is being used as part of the Raptor system at Scott Air Force Base.
Publications (1)
C. Frueh and A. Zakhor, "An Automated Method for Large-Scale, Ground-Based City Model Acquisition" in International Journal of Computer Vision, Vol. 60, No. 1, October 2004, pp. 5 - 24.
C. Frueh and A. Zakhor, "Constructing 3D City Models by Merging Ground-Based and Airborne Views" in Computer Graphics and Applications, November/December 2003, pp. 52 - 61.
C. Frueh and A. Zakhor, "Reconstructing 3D City Models by Merging Ground-Based and Airborne Views", Proceedings of the VLBV, September 2003, pp. 306 - 313 Madrid, Spain
C. Frueh, R. Sammon, and A. Zakhor, "Automated Texture Mapping of 3D City Models With Oblique Aerial Imagery" in 2nd International Symposium on 3D Data Processing, Visualization, and Transmission, 2004.
U. Neumann, “Approaches to Large-Scale Urban Modeling” in IEEE computer Graphics and applications
U. Neumann, “Visualizing Reality in an Augmented Virtual Environment” , acepted in Presence
U. Neumann, “Augmented Virtual Environments for Visualization of Dynamic Imagery”, accepted in IEEE Computer Graphics and Applications.
Publications (2)
U. Neumann, “Urban Site Modleing from LIDA”, CGGM’03
U. Neumann, “Augmented Virtual Environments (AVE): Dynamic Fusion of Imagery and 3D models”, VR 2003
U. Neumann, “3D Video Surveillance with Augmented Virtual Environments”, accepted in SIGGM 2003.
Sanjit Jhala and Suresh K. Lodha, ``Stereo and Lidar-Based Pose Estimation with Uncertainty for 3D Reconstruction'', To appear in the Proceedings of Vision, Visualization, and Modeling Conference, Stanford, Palo Alto, CA November 2004.
Hemantha Singamsetty and Suresh K. Lodha, ``An Integrated Geospatial Data Acquisition System for Reconstructing 3D Environments'', To appear in the Proceedings of the IASTED Conference on Advances in Computer Science and Technology (ACST), St. Thomas, Virgin Islands, USA, November 2004.
Publications (3)
Amin Charaniya, Roberto Manduchi, and Suresh K. Lodha, ``Supervised Parametric Classification of Aerial LiDAR Data", Proceedings of the IEEE workshop on Real-Time 3D Sensors and Their Use, Washington DC, June 2004.
Sanjit Jhala and Suresh K. Lodha, ``On-line Learning of Motion Patterns using an Expert Learning Framework", Proceedings of the IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Washington DC, June 2004.
Srikumar Ramalingam, Suresh K. Lodha, and Peter Sturm, ``A Generic Structure-from-Motion Algorithm for Cross-Camera Scenarios'', Proceedings of the OmniVis (Omnidirectional Vision, Camera Networks, and Non-Classical Cameras) Conference, Prague, Czech Republic, May 2004.
Srikumar Ramalingam and Suresh K. Lodha ``Adaptive Enhancement of 3D Scenes using Hierarchical Registration of Texture-Mapped Models", Proceedings of 3DIM Conference, IEEE Computer Society Press, Banff, Alberta, Canada, October 2003, pp.~203-210.
Publications (4)
Suresh K. Lodha, Nikolai M. Faaland, and Jose Renteria,``Hierarchical Topology Preserving Compression of 2D Vector Fields using Bintree and Triangular Quadtrees'', IEEE Transactions on Visualization and Computer Graphics, Vol. 9, No. 4, October 2003, pages 433--442.
Suresh K. Lodha, Krishna M. Roskin, and Jose C. Renteria, ``Hierarchical Topology Preserving Simplification of Terrains", Visual Computer, Vol. 19, No. 6, September 2003.
Suresh K. Lodha, Nikolai M. Faaland, Grant Wong, Amin P. Charaniya, Srikumar
Ramalingam, Arthur Keller, ``Consistent Visualization and Querying of Spatial Databases by a Location-Aware Mobile Agent'', Proceedings of Computer Graphics International (CGI), pp.~248--253, IEEE Computer Society Press, Tokyo, Japan, July 2003.
Christopher Campbell, Michael M. Shafae, Suresh K. Lodha and Dominic W. Massaro,
``Discriminating Visible Speech Tokens using Multi-Modality'', Proceedings of the International Conference on Auditory Display (ICAD), pp.~13--16, Boston, MA, July 2003.
Publications (5)
Amin Charaniya and Suresh K. Lodha, ``Speech Interface for Geo-Spatial Visualization'', Proceedings for the Conference on Computer Science and Technology (CST), Cancun, Mexico, May 2003.
William Ribarsky, editor (with Holly Rushmeier). 3D Reconstruction and Visualization of Large Scale Environments. Special Issue of IEEE Computer Graphics & Applications (December, 2003).
Justin Jang, Peter Wonka, William Ribarsky, and C.D. Shaw. Punctuated Simplification of Man-Made Objects. Submitted to The Visual Computer.
Tazama St. Julien, Joseph Scoccinaro, Jonathan Gdalevich, and William Ribarsky. Sharing of Precise 4D Annotations in Collaborative Mobile Situational Visualization. To be submitted, IEEE Symposium on Wearable Computing.
Ernst Houtgast, Onno Pfeiffer, Zachary Wartell, William Ribarsky, and Frits Post. Navigation and Interaction in a Multi-Scale Stereoscopic Environment. Submitted to IEEE Virtual Reality 2004.
Publications (6)
G.L. Foresti, C.S. Regazzoni and P.K. Varshney (Eds.), Multisensor Surveillance Systems : The Fusion Perspective , Kluwer Academic Press, 2003.
R. Niu, P. Varshney, K. Mehrotra and C. Mohan, ``Sensor Staggering in Multi-Sensor Target Tracking Systems'', Proceedings of the 2003 IEEE Radar Conference, Huntsville AL, May 2003.
L. Snidaro, R. Niu, P. Varshney, and G.L. Foresti, ``Automatic Camera Selection and Fusion for Outdoor Surveillance under Changing Weather Conditions'', Proceedings of the 2003 IEEE International Conference on Advanced Video and Signal Based Surveillance, Miami FL, July 2003.
H. Chen, P. K. Varshney, and M.A. Slamani, "On Registration of Regions of Interest (ROI) in Video Sequences" Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, CD-ROM, Miami, FL, July 21-22, 2003.
R.Niu and P.K.Varshney, “Target Location Estimation in Wireless Sensor Networks Using Binary Data,”Proceedings of the 38th Annual Conference on Information Sciences and Systems, Princeton, NJ, March 2004.
Publications (7)
L. Snidaro, R. Niu, P. Varshney, and G.L. Foresti, ``Sensor Fusion for Video Surveillance'', Proceedings of the Seventh International Conference on Information Fusion, Stockholm, Sweden, June 2004.
E. Elbasi, L. Zuo, K. Mehrotra, C. Mohan and P. Varshney, "Control Charts Approach for Scenario Recognition in Video Sequences," in Proc. Turkish Artificial Intelligence and Neural Networks Symposium(TAINN'04), June 2004.
M. Xu, R. Niu, and P. Varshney, `` Detection and Tracking of Moving Objects in Image Sequences with Varying Illumination'', to appear in Proceedings of the 2004 IEEE International Conference on Image Processing, Singapore, October 2004.
R. Rajagopalan, C.K. Mohan, K. Mehrotra and P.K. Varshney,"Evolutionary Multi-Objective Crowding Algorithm for Path Computations," to appear in Proc. International Conf. on Knowledge Based Computer Systems (KBCS-2004), Dec. 2004.
Future Work
• Important to make sense of the “world”, not just model it or visualize it
•Tons of data being collected by a variety of sensors all over the globe all the time
•How to process or digest the data in order to:
•Recognize significant events
•Make decisions despite uncertainty, and take actions
•Current MURI most concerned about “presenting” the data to military commanders in an uncluttered way visualization
•Future work on how to automatically construct the “big picture” of what is happening by combining a variety of modalities of data Audio, video, 3D models, sensors, pictures,
Battlefield Analysis
Distributed sensors
Physical layer Processing
Model / UpdateEnvironment
Visualize
Analysis/reasoning
Recognizeevents
Accomplish tasks
Make decisionTake actions
All of thisChangingDynamicallyWith time
Outline of Talks
9:00 - 9:15 Avideh Zakhor, U.C. Berkeley, "Overview" 9:15 - 10:00 Chris Frueh and Avideh Zakhor, U.C. Berkeley, "3D modeling and visualization of static and
dynamic scenes" 10:00 - 10:45 Ulrich Neuman, U.S.C. "Data Fusion in Augmented Virtual Environments" 10:45 - 11:30 Bill Ribarsky, Georgia Tech "Testbed and Results for Mobile Augmented Battlefield Visualization" 1:00 - 1:45 Suresh Lohda, U.C. Santa Cruz "Uncertainty in Data Classification, Pose Estimation
and 3D Reconstruction for Cross-Camera and Multiple Sensor Scenarios” 1:45 - 2:30 Pramod Varshney, Syracuse University
"Decision Making and Reasoning with Uncertain Image and Sensor Data"