1,983 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Analysis of the hands in egocentric vision: A survey
Egocentric vision (a.k.a. first-person vision - FPV) applications have
thrived over the past few years, thanks to the availability of affordable
wearable cameras and large annotated datasets. The position of the wearable
camera (usually mounted on the head) allows recording exactly what the camera
wearers have in front of them, in particular hands and manipulated objects.
This intrinsic advantage enables the study of the hands from multiple
perspectives: localizing hands and their parts within the images; understanding
what actions and activities the hands are involved in; and developing
human-computer interfaces that rely on hand gestures. In this survey, we review
the literature that focuses on the hands using egocentric vision, categorizing
the existing approaches into: localization (where are the hands or parts of
them?); interpretation (what are the hands doing?); and application (e.g.,
systems that used egocentric hand cues for solving a specific problem).
Moreover, a list of the most prominent datasets with hand-based annotations is
provided
Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey
Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research
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