1. Next Generation 4-D Distributed Modeling and Visualization of Battlefield Next Generation 4-D Distributed Modeling and Visualization of Battlefield Avideh Zakhor UC Berkeley September 2004

2. Participants yAvideh Zakhor, (UC Berkeley) yBill Ribarsky, (Georgia Tech) yUlrich Neumann (USC) yPramod Varshney (Syracuse) ySuresh Lodha (UC Santa Cruz)

3. Battlefield Visualization zDetailed, timely and accurate picture of the modern battlefield vital to military zMany sources of info to build “picture”: yArchival data, roadmaps, GIS and databases: static ySensor information from mobile agents at different times and locations yScene itself time varying; moving objects yMultiple modalities: fusion zHow to make sense of all these without information overload?

4. Visualization Pentagon Decision Making under Uncertainty Decision Making under Uncertainty Processing/ Visualization Uncertainty Processing/ Visualization 4D Modeling/ Update 4D Modeling/ Update Visualization and rendering Visualization and rendering Tracking/ Registration Tracking/ Registration

5. Research Agenda for 2003- 2004 z Modeling z Visualization and Rendering yMobile situational visualization yAugmented virtual environments z Add the temporal dimension (4D): yTracking of moving objects in scenes yModeling of time varying objects and scenes yDynamic event analysis, and recognition zPath planning under uncertainty

6. 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

7. Captured IR Frames Horizontal line scans from top to bottom at about 1 Hz

8. 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

9. Processing steps Compute depth at the horizontal line Track computed depth values along vertical lines Intraframe and interframe tracking Dense depth estimation

10. Results Depth video Color video

11.  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

12. Video Scene Analysis: Activity Classification with Uncertainty zExample activities: sitting, bending and standing zThe blue pointer indicates the level of certainty in the classifier decision a b c d

13. Audio Enhanced Visual Processing with Uncertainty Video Processing and Classification Audio Processing and Classification Visualization Description Generation Video Acquisition Sound Acquisition Fusion Uncertaint y

14.  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

15. Mobile Situational Visualization System Drawing Area Buttons Pen Tool Mobile Team Collaboration Example collaborators Shared observations of vehicle location, direction, speed

16. Goal Source Optimal route planning for battlefield risk minimization High risk Moderate risk Low risk Risk free

17. Lidar Data Classification Using height and height variation Using LiDAR data (no aerial image) Using all five features

18. Adaptive Stereo/Lidar based registration for modeling outdoor scenes Aerial view Stereo Based Registration LiDAR Based Registration LiDAR based approach seems better at turns. Stereo based approach captures terrain undulations

19. 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

20. 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

21. 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

22. 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.

23. Publications (1) z C. Fr ue h 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. z C. Fr ue h 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. z 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 z C. Fr ue h, 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. zU. Neumann, “Approaches to Large-Scale Urban Modeling” in IEEE computer Graphics and applications zU. Neumann, “Visualizing Reality in an Augmented Virtual Environment”, acepted in Presence zU. Neumann, “Augmented Virtual Environments for Visualization of Dynamic Imagery”, accepted in IEEE Computer Graphics and Applications.

24. Publications (2) z U. Neumann, “Urban Site Modleing from LIDA”, CGGM’03 z U. Neumann, “Augmented Virtual Environments (AVE): Dynamic Fusion of Imagery and 3D models”, VR 2003 z U. Neumann, “3D Video Surveillance with Augmented Virtual Environments”, accepted in SIGGM 2003. z 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. z 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.

25. Publications (3) z 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. z 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. z 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. z 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.

26. Publications (4) z 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. z Suresh K. Lodha, Krishna M. Roskin, and Jose C. Renteria, ``Hierarchical Topology Preserving Simplification of Terrains", Visual Computer, Vol. 19, No. 6, September 2003. z Suresh K. Lodha, Nikolai M. Faaland, Grant Wong, Amin P. Charaniya, Srikumar z 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. z Christopher Campbell, Michael M. Shafae, Suresh K. Lodha and Dominic W. Massaro, z ``Discriminating Visible Speech Tokens using Multi-Modality'', Proceedings of the International Conference on Auditory Display (ICAD), pp.~13--16, Boston, MA, July 2003.

27. Publications (5) z 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. z William Ribarsky, editor (with Holly Rushmeier). 3D Reconstruction and Visualization of Large Scale Environments. Special Issue of IEEE Computer Graphics & Applications (December, 2003). z Justin Jang, Peter Wonka, William Ribarsky, and C.D. Shaw. Punctuated Simplification of Man-Made Objects. Submitted to The Visual Computer. z 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. z 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.

28. Publications (6) z G.L. Foresti, C.S. Regazzoni and P.K. Varshney (Eds.), Multisensor Surveillance Systems : The Fusion Perspective, Kluwer Academic Press, 2003. z 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. z 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. z 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. z 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.

29. Publications (7) z 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. z 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. z 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. z 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.

30. 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,

31. Battlefield Analysis Distributed sensors Physical layer Processing Model / Update Environment Visualize Analysis/reasoning Recognize events Accomplish tasks Make decision Take actions All of this Changing Dynamically With time

32. Outline of Talks z9:00 - 9:15 Avideh Zakhor, U.C. Berkeley, "Overview" z9:15 - 10:00 Chris Frueh and Avideh Zakhor, U.C. Berkeley, "3D modeling and visualization of static and dynamic scenes" z10:00 - 10:45 Ulrich Neuman, U.S.C. "Data Fusion in Augmented Virtual Environments" z10:45 - 11:30 Bill Ribarsky, Georgia Tech "Testbed and Results for Mobile Augmented Battlefield Visualization" z1:00 - 1:45 Suresh Lohda, U.C. Santa Cruz "Uncertainty in Data Classification, Pose Estimation and 3D Reconstruction for Cross-Camera and Multiple Sensor Scenarios” z1:45 - 2:30 Pramod Varshney, Syracuse University "Decision Making and Reasoning with Uncertain Image and Sensor Data"