Journal ArticleDOI
First-Person Vision
Takeo Kanade,Martial Hebert +1 more
- Vol. 100, Iss: 8, pp 2442-2453
TLDR
This paper argues that the first-person vision (FPV), which senses the environment and the subject's activities from a wearable sensor, is more advantageous with images about thesubject's environment as taken from his/her view points, and with readily available information about head motion and gaze through eye tracking.Abstract:
For understanding the behavior, intent, and environment of a person, the surveillance metaphor is traditional; that is, install cameras and observe the subject, and his/her interaction with other people and the environment. Instead, we argue that the first-person vision (FPV), which senses the environment and the subject's activities from a wearable sensor, is more advantageous with images about the subject's environment as taken from his/her view points, and with readily available information about head motion and gaze through eye tracking. In this paper, we review key research challenges that need to be addressed to develop such FPV systems, and describe our ongoing work to address them using examples from our prototype systems.read more
Citations
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Proceedings ArticleDOI
Delving into egocentric actions
Yin Li,Zhefan Ye,James M. Rehg +2 more
TL;DR: A novel set of egocentric features are presented and shown how they can be combined with motion and object features and a significant performance boost over all previous state-of-the-art methods is uncovered.
Proceedings ArticleDOI
Learning to Predict Gaze in Egocentric Video
TL;DR: A model for gaze prediction in egocentric video is presented by leveraging the implicit cues that exist in camera wearer's behaviors and model the dynamic behavior of the gaze, in particular fixations, as latent variables to improve the gaze prediction.
Journal ArticleDOI
The Evolution of First Person Vision Methods: A Survey
TL;DR: This paper summarizes the evolution of the state of the art in FPV video analysis between 1997 and 2014, highlighting, among others, the most commonly used features, methods, challenges, and opportunities within the field.
Journal ArticleDOI
Computer vision for assistive technologies
TL;DR: An original "task oriented" way to categorize the state of the art of the AT works has been introduced that relies on the split of the final assistive goals into tasks that are then used as pointers to the works in literature in which each of them has been used as a component.
Proceedings ArticleDOI
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention
TL;DR: Rulstm as mentioned in this paper proposes an architecture able to anticipate actions at multiple temporal scales using two LSTMs to summarize the past and formulate predictions about the future, which is ranked first in the public leaderboard of the EPIC-Kitchens egocentric action anticipation challenge 2019.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.