Enhancing communication and dramatic impact of online live performance with cooperative audience control

Yonezawa, Takeshi; Tokuda, Hideyuki

doi:10.1145/2370216.2370234

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…In and Seering [69], who describe a range of audience roles in live complement, Smith [73] contends that agency is "the most intereststreaming; helpers, power seekers, collaborators, solipsists, and ing part of ...live streaming...broadcaster and spectator -phenometrolls. For instance, Yonezawa [91] suggests that non-verbal comnon...the malleability of activity and passivity. " munication and interaction can promote camaraderie amongst au-We found four themes common to the Agency sensitizing condiences and performers [91].…”

Section: Agencymentioning

confidence: 99%

“…For instance, Yonezawa [91] suggests that non-verbal comnon...the malleability of activity and passivity. " munication and interaction can promote camaraderie amongst au-We found four themes common to the Agency sensitizing condiences and performers [91]. Conversely, audience agency through cept: live-streaming agency, interaction, choice, and control.…”

Section: Agencymentioning

confidence: 99%

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Mapping Design Spaces for Audience Participation in Game Live Streaming

Striner

Webb

Hammer

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Live streaming sites such as Twitch ofer new ways for remote audiences to engage with and afect gameplay. While research has considered how audiences interact with games, HCI lacks clear demarcations of the potential design spaces for audience participation. This paper introduces and validates a theme map of audience participation in game live streaming for student designers. This map is a lens that reveals relationships among themes and sub-themes of Agency, Pacing, and Community-to explore, refect upon, describe, and make sense of emerging, complex design spaces. We are the frst to articulate such a lens, and to provide a refective tool to support future research and education. To create the map, we perform a thematic analysis of design process documents of a course on audience participation for Twitch, using this analysis to visually coordinate relationships between important themes. To help student designers analyze and refect on existing experiences, we supplement the theme map with a set of mapping procedures. We validate the applicability of our map with a second set of student designers, who found the map useful as a comparative and refective tool.

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Section: Agencymentioning

confidence: 99%

Section: Agencymentioning

confidence: 99%

Mapping Design Spaces for Audience Participation in Game Live Streaming

Striner

Webb

Hammer

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…As related work on manipulating dynamic contents, Yonezawa [1] has proposed a remote video control system for NicoNico Live Video 9 . It is not a content curation but it allows viewers over the Internet to control the angle and zoom level of camera according to their preferences.…”

Section: A Existing Studies For Content Curationmentioning

confidence: 99%

“…Recently, live video streaming applications such as Meercat 1 and Periscope 2 have been available. Such realtime CGM (Consumer Generated Media) will receive increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Automatic Content Curation System for Multiple Live Sport Video Streams

Fujisawa

Hirabe

Suwa

et al. 2015

2015 IEEE International Symposium on Multimedia (ISM)

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In this paper, we aim to develop a method to create personalized and high-presence multi-channel contents for a sport game through realtime content curation from various media streams captured/created by spectators. We use the live TV broadcast as a ground truth data and construct a machine learning-based model to automatically conduct curation from multiple videos which spectators captured from different angles and zoom levels. The live TV broadcast of a baseball game has some curation rules which select a specific angle camera for some specific scenes (e.g., a pitcher throwing a ball). As inputs for constructing a model, we use meta data such as image feature data (e.g., a pitcher is on the screen) in each fixed interval of baseball videos and game progress data (e.g., the inning number and the batting order). Output is the camera ID (among multiple cameras of spectators) at each point of time. For evaluation, we targeted Spring-Selection high-school baseball games. As training data, we used image features, game progress data, and the camera position at each point of time in the TV broadcast. We used videos of a baseball game captured from 7 different points in Hanshin Koshien Stadium with handy video cameras and generated sample data set by dividing the videos to fixed interval segments. We divided the sample data set into the training data set and the test data set and evaluated our method through two validation methods: (1) 10-fold crossvalidation method and (2) hold-out methods (e.g., learning first and second innings and testing third inning). As a result, our method predicted the camera switching timings with accuracy (F-measure) of 72.53% on weighted average for the base camera work and 92.1% for the fixed camera work.

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“…To enhance such live broadcasting experiences, previous research focused on merging verbal communication [14] and non-verbal communication [6,15]. However, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

SENSeTREAM

Yonezawa

Ogawa

Kyono

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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We propose a novel technique that aggregates multiple sensor streams generated by totally different types of sensors into a visually enhanced video stream. This paper shows major features of SENSeTREAM and demonstrates enhancement of user experience in an online live music event. Since SENSeTREAM is a video stream with sensor values encoded in a two-dimensional graphical code, it can transmit multiple sensor data streams while maintaining their synchronization. A SENSeTREAM can be transmitted via existing live streaming services, and can be saved into existing video archive services. We have implemented a prototype SENSe-TREAM generator and deployed it to an online live music event. Through the pilot study, we confirmed that SENSe-TREAM works with popular streaming services, and provide a new media experience for live performances. We also indicate future direction for establishing visual stream aggregation and its applications.

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Enhancing communication and dramatic impact of online live performance with cooperative audience control

Cited by 13 publications

References 15 publications

Mapping Design Spaces for Audience Participation in Game Live Streaming

Mapping Design Spaces for Audience Participation in Game Live Streaming

Automatic Content Curation System for Multiple Live Sport Video Streams

SENSeTREAM

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