65,035 research outputs found
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods
SAVASA project @ TRECVID 2012: interactive surveillance event detection
In this paper we describe our participation in the interactive surveillance event detection task at TRECVid 2012. The system we developed was comprised of individual classifiers brought together behind a simple video search interface that enabled users to select relevant segments based on down~sampled animated gifs. Two types of user -- `experts' and `end users' -- performed the evaluations. Due to time constraints we focussed on three events -- ObjectPut, PersonRuns and Pointing -- and two of the five available cameras (1 and 3). Results from the interactive runs as well as discussion of the performance of the underlying retrospective classifiers are presented
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
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