An Egocentric Look at Video Photographer Identity

Hoshen, Yedid; Peleg, Bezalel

doi:10.1109/cvpr.2016.464

Cited by 34 publications

(21 citation statements)

References 24 publications

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“…However, some work shows that ego-actions (like riding a bus, snowboarding, etc.) are detectable from the scene video [19,15], and the walking style of the camera wearer can even aid person identification [8]. We consider whether egovideo can go further to reveal full 3D body pose.…”

Section: Related Workmentioning

confidence: 99%

Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video

Jiang

Grauman

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

101

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Understanding the camera wearer's activity is central to egocentric vision, yet one key facet of that activity is inherently invisible to the camerathe wearer's body pose. Prior work focuses on estimating the pose of hands and arms when they come into view, but this 1) gives an incomplete view of the full body posture, and 2) prevents any pose estimate at all in many frames, since the hands are only visible in a fraction of daily life activities. We propose to infer the "invisible pose" of a person behind the egocentric camera. Given a single video, our efficient learning-based approach returns the full body 3D joint positions for each frame. Our method exploits cues from the dynamic motion signatures of the surrounding scene-which changes predictably as a function of body pose-as well as static scene structures that reveal the viewpoint (e.g., sitting vs. standing). We further introduce a novel energy minimization scheme to infer the pose sequence. It uses soft predictions of the poses per time instant together with a nonparametric model of human pose dynamics over longer windows. Our method outperforms an array of possible alternatives, including deep learning approaches for direct pose regression from images.

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Section: Related Workmentioning

confidence: 99%

Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video

Jiang

Grauman

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

101

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“…The first type are videos sharing similar camera path on different times. We obtained the dataset of [39] suitable for this purpose. The second type are videos shot simultaneously by number of people wearing cameras and walking together.…”

Section: B Panoramic Hyperlapsementioning

confidence: 99%

EgoSampling: Wide View Hyperlapse From Egocentric Videos

Halperin

Poleg

Arora

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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The possibility of sharing one's point of view makes use of wearable cameras compelling. These videos are often long, boring and coupled with extreme shake, as the camera is worn on a moving person. Fast forwarding (i.e. frame sampling) is a natural choice for quick video browsing. However, this accentuates the shake caused by natural head motion in an egocentric video, making the fast forwarded video useless. We propose EgoSampling, an adaptive frame sampling that gives stable, fast forwarded, hyperlapse videos. Adaptive frame sampling is formulated as an energy minimization problem, whose optimal solution can be found in polynomial time. We further turn the camera shake from a drawback into a feature, enabling the increase in field-of-view of the output video. This is obtained when each output frame is mosaiced from several input frames. The proposed technique also enables the generation of a single hyperlapse video from multiple egocentric videos, allowing even faster video consumption.

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“…This makes our method perform well even with videos captured under significant head motion. Hoshen and Peleg [30] have attempted to identify individuals by learning their head motion patterns. In contract to their approach that requires head motion classifiers to be trained per individual before identification, our work uses generic targetness to guide correlation-based identification, which does not have to be learned for specific individuals.…”

Section: Related Workmentioning

confidence: 99%

Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures

Yonetani

Sato

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We envision a future time when wearable cameras are worn by the masses and recording first-person point-of-view videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera -with or without consent. Motivated by these benefits and risks, we developed a self-search technique tailored to first-person videos. The key observation of our work is that the egocentric head motion of a target person (i.e., the self) is observed both in the point-of-view video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to identify instances of the self uniquely. We incorporate this feature into the proposed approach that computes the motion correlation over densely-sampled trajectories to search for a target individual in observer videos. Our approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, target video retrieval, and social group clustering.

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An Egocentric Look at Video Photographer Identity

Cited by 34 publications

References 24 publications

Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video

Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video

EgoSampling: Wide View Hyperlapse From Egocentric Videos

Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures

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