Wisdom of the Crowd in Egocentric Video Curation

Hoshen, Yedid; Ben-Artzi, Gil; Peleg, Bezalel

doi:10.1109/cvprw.2014.90

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…To explore the relationship among multiple egocentric viewers, [25] combines several egocentric videos to achieve a more complete video with less quality degradation by estimating the importance of different scene regions and incorporating the consensus among several egocentric videos. Fathi et al, [26] detect and recognize the type of social interactions such as dialogue, monologue, and discussion by detecting human faces and estimating their body and head orientations.…”

Section: Social Interactions Among Egocentric Viewersmentioning

confidence: 99%

Egocentric Meets Top-View

Ardeshir

Borji

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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Thanks to the availability of wearable devices such as GoPro cameras, smart phones, and glasses, we have now access to a plethora of videos captured from the first person perspective. Surveillance cameras and Unmanned Aerial Vehicles (UAVs) also offer tremendous amounts of video data recorded from top and oblique view points. Egocentric and surveillance vision have been studied extensively but separately in the computer vision community. The relationship between these two domains, however, remains unexplored. In this study, we make the first attempt in this direction by addressing two basic yet challenging questions. First, having a set of egocentric videos and a top-view video, does the top-view video contain all or some of the egocentric viewers? In other words, have these videos been shot in the same environment at the same time? Second, if so, how can we identify the egocentric viewers in the top-view video? These problems can become even more challenging when videos are not temporally aligned. We model each view using a graph, and compute the assignment and time-delays in an iterative-alternative fashion using spectral graph matching and time delay estimation. Such an approach handles the temporal misalignment between the egocentric videos and the top-view video.

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Section: Social Interactions Among Egocentric Viewersmentioning

confidence: 99%

Egocentric Meets Top-View

Ardeshir

Borji

2019

IEEE Trans. Pattern Anal. Mach. Intell.

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“…To explore the relationship among multiple egocentric viewers, [18] combines several egocentric videos to achieve a more complete video with less quality degradation by estimating the importance of different scene regions and incorporating the consensus among several egocentric videos. Fathi et al, [19] detect and recognize the type of social interactions such as dialogue, monologue, and discussion by detecting human faces and estimating their body and head orientations.…”

Section: Social Interactions Among Egocentric Viewersmentioning

confidence: 99%

Ego2Top: Matching Viewers in Egocentric and Top-View Videos

Ardeshir

Borji

2016

Lecture Notes in Computer Science

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Abstract. Egocentric cameras are becoming increasingly popular and provide us with large amounts of videos, captured from the first person perspective. At the same time, surveillance cameras and drones offer an abundance of visual information, often captured from top-view. Although these two sources of information have been separately studied in the past, they have not been collectively studied and related. Having a set of egocentric cameras and a top-view camera capturing the same area, we propose a framework to identify the egocentric viewers in the top-view video. We utilize two types of features for our assignment procedure. Unary features encode what a viewer (seen from top-view or recording an egocentric video) visually experiences over time. Pairwise features encode the relationship between the visual content of a pair of viewers. Modeling each view (egocentric or top) by a graph, the assignment process is formulated as spectral graph matching. Evaluating our method over a dataset of 50 top-view and 188 egocentric videos taken in different scenarios demonstrates the efficiency of the proposed approach in assigning egocentric viewers to identities present in top-view camera. We also study the effect of different parameters such as the number of egocentric viewers and visual features. An extended abstract of this effort has been reported in [1].

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“…Rather than inject hand-crafted rules, we propose to learn what constitutes an engagement interval. Recent methods explore ways to predict the "importance" of spatial regions (objects, people) using cues like hand detection and frame centrality [28], [29], detect novelty [30], and infer "social saliency" when multiple cameras capture the same event [31]- [33]. We tackle engagement, not summarization, though likely our predictions could be another useful input to a summarization system.…”

Section: First-person Activity and Summarizationmentioning

confidence: 99%

Detecting Engagement in Egocentric Video

Grauman

2016

Computer Vision – ECCV 2016

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In a wearable camera video, we see what the camera wearer sees. While this makes it easy to know roughly what he chose to look at, it does not immediately reveal when he was engaged with the environment. Specifically, at what moments did his focus linger, as he paused to gather more information about something he saw? Knowing this answer would benefit various applications in video summarization and augmented reality, yet prior work focuses solely on the "what" question (estimating saliency, gaze) without considering the "when" (engagement). We propose a learning-based approach that uses long-term egomotion cues to detect engagement, specifically in browsing scenarios where one frequently takes in new visual information (e.g., shopping, touring). We introduce a large, richly annotated dataset for ego-engagement that is the first of its kind. Our approach outperforms a wide array of existing methods. We show engagement can be detected well independent of both scene appearance and the camera wearer's identity.

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Wisdom of the Crowd in Egocentric Video Curation

Cited by 20 publications

References 20 publications

Egocentric Meets Top-View

Egocentric Meets Top-View

Ego2Top: Matching Viewers in Egocentric and Top-View Videos

Detecting Engagement in Egocentric Video

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