705 research outputs found
Summarizing First-Person Videos from Third Persons' Points of Views
Video highlight or summarization is among interesting topics in computer
vision, which benefits a variety of applications like viewing, searching, or
storage. However, most existing studies rely on training data of third-person
videos, which cannot easily generalize to highlight the first-person ones. With
the goal of deriving an effective model to summarize first-person videos, we
propose a novel deep neural network architecture for describing and
discriminating vital spatiotemporal information across videos with different
points of view. Our proposed model is realized in a semi-supervised setting, in
which fully annotated third-person videos, unlabeled first-person videos, and a
small number of annotated first-person ones are presented during training. In
our experiments, qualitative and quantitative evaluations on both benchmarks
and our collected first-person video datasets are presented.Comment: 16+10 pages, ECCV 201
Detecting Hands in Egocentric Videos: Towards Action Recognition
Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extreme illumination changes in egocentric images, hand
detection is not a trivial task because of the intrinsic large variability of
hand appearance. We propose a hand detector that exploits skin modeling for
fast hand proposal generation and Convolutional Neural Networks for hand
recognition. We tested our method on UNIGE-HANDS dataset and we showed that the
proposed approach achieves competitive hand detection results
Recognition of Activities of Daily Living with Egocentric Vision: A Review.
Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory
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