Crowdsourced automatic zoom and scroll for video retargeting

Carlier, Aurélie; Charvillat, Vincent; Ooi, Wei Tsang; Grigoras, Romulus; Morin, Géraldine

doi:10.1145/1873951.1873962

Cited by 33 publications

(21 citation statements)

References 17 publications

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“…Shaw and Davis advocate using actual viewership data in modeling user interest [24]. Existing systems leverage scrubbing [29], zooming and panning [3], and playing and pausing [4] activities. SocialSkip [4] demonstrates that modeling users' video interactions can accurately capture user interest in information retrieval tasks.…”

Section: Related Workmentioning

confidence: 99%

Understanding in-video dropouts and interaction peaks inonline lecture videos

Kim¹,

Guo

Seaton

et al. 2014

Proceedings of the First ACM Conference on Learning @ Scale Conference

304

208

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With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in rewatching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs.

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Section: Related Workmentioning

confidence: 99%

Understanding in-video dropouts and interaction peaks inonline lecture videos

Kim¹,

Guo

Seaton

et al. 2014

Proceedings of the First ACM Conference on Learning @ Scale Conference

304

208

View full text Add to dashboard Cite

show abstract

“…To model user interest in video watching, researchers have proposed features such as viewership [38], scrubbing [39], zooming and panning [7], and replaying and skipping [9] activities. SocialSkip [9] applied signal processing to replaying activity data in order to infer interesting video segments.…”

Section: Leveraging Interaction Historymentioning

confidence: 99%

“…Request permissions from permissions@acm.org. UIST'14, October [5][6][7][8]2014, Honolulu, HI, USA. Copyright is held by the owner/author(s).…”

Section: Introductionmentioning

confidence: 99%

Data-driven interaction techniques for improving navigation of educational videos

Kim

Guo

Cai

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

138

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With an unprecedented scale of learners watching educational videos on online platforms such as MOOCs and YouTube, there is an opportunity to incorporate data generated from their interactions into the design of novel video interaction techniques. Interaction data has the potential to help not only instructors to improve their videos, but also to enrich the learning experience of educational video watchers. This paper explores the design space of data-driven interaction techniques for educational video navigation. We introduce a set of techniques that augment existing video interface widgets, including: a 2D video timeline with an embedded visualization of collective navigation traces; dynamic and non-linear timeline scrubbing; data-enhanced transcript search and keyword summary; automatic display of relevant still frames next to the video; and a visual summary representing points with high learner activity. To evaluate the feasibility of the techniques, we ran a laboratory user study with simulated learning tasks. Participants rated watching lecture videos with interaction data to be efficient and useful in completing the tasks. However, no significant differences were found in task performance, suggesting that interaction data may not always align with moment-by-moment information needs during the tasks.

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“…In context of video editing in a studio environment [31], collective user behavior has been proven an effective way to understand and collaborate on video. The benefits of collective intelligence for web video have been noted by Carlier et al [25], in the case of zoom-able video user interface. Yew et al [20] have recognized the importance of scrubs (fast forward and rewind), but they have only included counts in their classifier and not the actual timing of the scrub events.…”

Section: Key-frame Detection Process Through Implicit User-interest Mmentioning

confidence: 91%

Collective intelligence within web video

Chorianopoulos

2013

Hum. Cent. Comput. Inf. Sci.

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We present a user-based approach for detecting interesting video segments through simple signal processing of users' collective interactions with the video player (e.g., seek/scrub, play, pause). Previous research has focused on content-based systems that have the benefit of analyzing a video without user interactions, but they are monolithic, because the resulting key-frames are the same regardless of the user preferences. We developed the open-source SocialSkip system on a modular cloud-based architecture and analyzed hundreds of user interactions within difficult video genres (lecture, how-to, documentary) by modeling them as user interest time series. We found that the replaying activity is better than the skipping forward one in matching the semantics of a video, and that all interesting video segments can be found within a factor of two times the average user skipping step from the local maximums of the replay time series. The concept of simple signal processing of implicit user interactions within video could be applied to any type of Web video system (e.g., TV, desktop, tablet), in order to improve the user navigation experience with dynamic and personalized key-frames.

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Crowdsourced automatic zoom and scroll for video retargeting

Cited by 33 publications

References 17 publications

Understanding in-video dropouts and interaction peaks inonline lecture videos

Understanding in-video dropouts and interaction peaks inonline lecture videos

Data-driven interaction techniques for improving navigation of educational videos

Collective intelligence within web video

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