23,970 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
Range entropy: A bridge between signal complexity and self-similarity
Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for
temporal complexity analysis of real-world phenomena. However, their
relationship with the Hurst exponent as a measure of self-similarity is not
widely studied. Additionally, ApEn and SampEn are susceptible to signal
amplitude changes. A common practice for addressing this issue is to correct
their input signal amplitude by its standard deviation. In this study, we first
show, using simulations, that ApEn and SampEn are related to the Hurst exponent
in their tolerance r and embedding dimension m parameters. We then propose a
modification to ApEn and SampEn called range entropy or RangeEn. We show that
RangeEn is more robust to nonstationary signal changes, and it has a more
linear relationship with the Hurst exponent, compared to ApEn and SampEn.
RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1
(minimum entropy) and it has no need for signal amplitude correction. Finally,
we demonstrate the clinical usefulness of signal entropy measures for
characterisation of epileptic EEG data as a real-world example.Comment: This is the revised and published version in Entrop
Face Identification and Clustering
In this thesis, we study two problems based on clustering algorithms. In the
first problem, we study the role of visual attributes using an agglomerative
clustering algorithm to whittle down the search area where the number of
classes is high to improve the performance of clustering. We observe that as we
add more attributes, the clustering performance increases overall. In the
second problem, we study the role of clustering in aggregating templates in a
1:N open set protocol using multi-shot video as a probe. We observe that by
increasing the number of clusters, the performance increases with respect to
the baseline and reaches a peak, after which increasing the number of clusters
causes the performance to degrade. Experiments are conducted using recently
introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
datasets
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Machine Analysis of Facial Expressions
No abstract
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