Wireless sensor networks for art & entertainment applications

1. Wireless sensor networks for art & entertainment applications Center for Embedded Networked Sensing April 18, 2003 Jeff Burke UCLA School of Theater, Film and Television jburke@hypermedia.ucla.edu

2. HyperMedia Studio Background • Founded in 1997 by film professor Fabian Wagmister. • Located in a re-assigned television studio and edit rooms in the UCLA Department of Film, Television and Digital Media. Behind the Bars (1999) Iliad Project Team (2002)

3. Goals Invocation and Interference (2001) • Investigate the impact of emerging technologies on traditional production of theater, film and television. • Explore new work inspired and enabled by their unique capabilities and qualities. • Build tools that help creators, educators, and students explore • novel uses of instrumented environments. Iliad Project Rehearsal(2002)

4. Arena • Installation artworks • Live performance • Educational spaces • Film production Behind the Bars (1999) Macbett (2001)

5. Outline • Background • Application examples • Live performance • Installation artworks • Film production • Research topics • Collaboration • Research • Curriculum

6. Ubicomp as a catalyst for creation • Ubiquitous computing technologies canmake action and presence in physical space relevant to computing, networks, and media. • Process • Presence • Context

7. Art as a laboratory for ubicomp • Controlled slices of the real world • Development / rehearsal process • need to author on-the-fly, in-the-field • Specific performance requirements different tradeoffs • Challenge of basic assumptions • different perspectives

8. Live performance • Responsive stage environments • Arizona State University’s Intelligent Stage (Lovell, et al.) • University of Georgia (Saltz) • MIT Media Lab (Sparacino, et al.) • Audience interaction / incorporation • Carnegie Mellon ETC • Blast Theory (UK) • Many other companies Fahrenheit 451 (2000)

9. Macbett Directed by Adam Shive, Interactive systems by Jeff Burke (2001) • Department of Theater subscription-series production. • Large-scale theatrical lighting and sound controlled based on actor movement, as sensed by an off-the-shelf wireless tracking system. • Team of one graduate student and four undergraduate programmers, as well as the entire design team, cast, and crew.

10. Macbett • Seven networked workstations and servers communicated via UDP for • Sensor management, • Feature calculation, • Lighting control, • Sound control, • “Authoring” of interactive relationships, • System monitoring.

11. Macbett

12. The Iliad Project Architecture: Jeff Burke, Jared Stein / Research direction: Fabian Wagmister, Edit Villareal, Jose Luis Valenzuela • An ongoing research project that involves the simultaneous development of an original theatrical script, design and acting technique, and technology. • Emerging focus Customization of the script and media based on the attending audience.

13. The Iliad Project • Core technologies • Audience database • Radio-Frequency Identification • Geographic Information System • Image capture and manipulation • Text processing • Live video streaming • Middleware – “Kolo”

14. Live performance • Sensor network research areas • Localization: robust, precise, and scalable • Actor (and audience) ‘state’ • Middleware • Monitoring and realtime feedback • Authoring tools

15. lights 1 2 intensity intensity ML001 intensity colorC colorM colorY focus pan tilt Middleware for experiementation • Lightweight, consistent access to inputs and outputs. • Very high-level abstractions can be counterproductive,unless they can be built by the author/creator. lights.ML001.colorM magenta level of moving light #1

16. tracking actors macbett macbett banco position x x x y y y z z z Middleware for experiementation • First, make input and output devices on the network available in a consistent way. • Let authors define hierarchies and abstractions. • or

17. lights.ML001, sound.pb1.left left lights sound syncCue1 intensity members ML001 intensity pb1 intensity colorC colorM colorY focus pan tilt Middleware for experiementation • Synchronization of actions in the environmentwithout centralized show control. • ‘group’

18. Middleware for experiementation • Managing connections • Support for constructing ‘real-time’ relationshipsbetween data sources and sinks. • Arbitration between competing relationships. • Kolo middleware • Java-based middleware API(device drivers in C/C++) • Scripting language

19. Specifying simple spatial relationships

20. Specifying simple spatial relationships

21. Specifying simple spatial relationships

22. Specifying simple spatial relationships

23. Installation artwork • Non-narrative participatory experiences • Without performance definitions of performer/audience • Extensions into media-rich educational spaces • Participatory simulations • Embodied and kinesthetic learning hamletmachine (2000-2) cheLA demonstration (2002)

24. Time & Time Again… Fabian Wagmister and Lynn Hershmann (1999) • Distributed interactive installation exploring complex relationships between our increasingly interlinked bodies and machines. • Site-specific work in the Ruhr region of Germany. • Live video streamed into musuem. • Real-time video compositing. • Silhouette image choice controlled by sensors in the installation space. • Robotic, telematic doll streaming images to and being controlled by web users. • History database of video fragments recorded by web visitors.

25. Time & Time Again… Fabian Wagmister and Lynn Hershmann (1999)

26. Behind the Bars Fabian Wagmister (1999) • Confrontational interactive environment treating Latin America’s history of physical and intellectual oppression. • Premiere: • Central American Film and Video Festival in Nicaragua.

27. Media-rich educational spaces • Research areas • Adapt a simple wireless ADC platform? • Middleware • Data storage / mining • Space configuration • Authoring tools

28. Film production • Equipping the film set with a wireless sensor network to support all aspects of production through • Instrumentation • Observation • ‘Augmented footage’ • Decision support • Control • Developing collaboration with CENS faculty.

29. Sensor networks on the film set • Not just application… laboratory: • ‘Articulated chaos’, • Complex coordination to generate small slices of controlled reality, • Tradition of precise documentation, • Repetitive action, • Rapid, large scale field deployments, • Rigorous test environment already available on our campus (over 100 student films per year).

30. Terminology • Script • Location • Scene • Shot • Take Exterior, Day, Suburban Street Closeup of Heather at the fruit stand

31. Three time frames

32. Editing in post-production

33. Filmmaking Application Drivers • Primary / initial • Asset management • Continuity management • Secondary / future • Real-time control • General post-production • Special effects

34. Platform • Wireless articulated imagers • Commercial x86 low-power architecture • Active tags • Low-cost, low-power tags providing RF and/or acoustic localization • Passive RFID tags • Primarily for asset management

35. Continuity Management • Ensuring repeatable action by the crew and talent. • ‘Script supervision’ is facilitated by several ‘continuity assistants’. • Fine to coarse grained documentation. • Squirrel twitches her nose in take 1a, but not in take 1d. • Male bystander correctly on the woman’s right in both shots.

36. Continuity / Asset Management • A superset of asset management: • Must track on-camera and off-camera items that create the film camera’s field of view for a shot: • Presence and qualities of props, scenery, and actors. • Equipment positions and settings.

37. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge

38. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge • Eventually, must localize O(100) objects for continuity on a large set. • Need for coarse-grained localization for O(1000) objects for asset tracking only is apparent. • Would like to know more than just location: orientation, gaze direction in closeups, etc.

39. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge • Data recorded must be ‘frame-accurate’, synchronized with the SMTPE time code of the shoot. • 24 frames/sec for film. • 29.97 frames/sec for NTSC video. • Reasonable but fine-grained synchronization of wireless sensor network to this external clock.

40. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge • All that matters is what the film camera sees – it is the ‘privileged viewpoint’ of the system. • Camera and film stock parameters can be used to estimate what is seen and what isn’t. • With this information, manage: • focus of attention, • power consumption, • processing cycles, • bandwidth, • sensor articulation.

41. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge • Need to track and take advantage of the repetitive action built into the process: • multiple shots of one scene, • multiple takes of one shot, • off-camera rehearsal. • Leverage filmmakers’ experience to develop heuristics for what action is ‘crucial’ and what is not.

42. Needs and constraints • Localization with multiple sensing modalities • Synchronization • Privileged viewpoint • Event repetition • The Script – a priori scene knowledge • Unlike many ubicomp scenarios, the film shoot has a carefully defined blueprint for the action that will occur: the script. • Could consider ‘script reading’ research, or… • Use the consistent format of film scripts to recognize who and what are (or will be, or was) present in a scene or shot.

43. Decision support example: the 180o rule

44. Decision support example: the 180o rule

45. Decision support example: the 180o line

46. Decision support example: the 180o rule • A simple position-based metric for perceived discontinuity of scene layouts from multiple perspectives. • Well-suited for real-time visualization. • Important use of ‘coarser’ grained continuity management.

47. Application Driver for What? • Localization and identification… • using several sensing platforms, high resolution for O(100) objects, low resolution for O(1000). • Asset management and tracking… • with multiple action-dependency relationships, and across multiple time frames. • Embedding sensor network generated metadata into media… • to create augmented footage. • Scene observation techniques… • for recognizing discontinuities across multiple perpsectives. • Field decision support… • considering an arbitrary ‘privileged viewpoint’.

48. Art as a laboratory for ubicomp • Controlled slices of the real world • Development / rehearsal process • need to author on-the-fly, in-the-field • Specific performance requirements different tradeoffs • Challenge of basic assumptions • different perspectives

49. Research Agenda • Short term goals • Localization platform • Middleware for ‘real-time’ relationships • Writing many input / output (and storage) drivers • Longer term interests • Fusing vision with other sensing modalities • Authoring tools

50. Collaboration • Research • Project-based focus • Commissions • Externally funded research • Students and faculty from EE/CS, Theater, Film/TV, Design • Curriculum • Existing courses • FTVD 144/244, Theater 144C • Planned courses • Concurrent TFT / Electrical Engineering