13,143 research outputs found
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.Comment: Accepted to IEEE Transactions on Image Processin
Challenges of Big Data Analysis
Big Data bring new opportunities to modern society and challenges to data
scientists. On one hand, Big Data hold great promises for discovering subtle
population patterns and heterogeneities that are not possible with small-scale
data. On the other hand, the massive sample size and high dimensionality of Big
Data introduce unique computational and statistical challenges, including
scalability and storage bottleneck, noise accumulation, spurious correlation,
incidental endogeneity, and measurement errors. These challenges are
distinguished and require new computational and statistical paradigm. This
article give overviews on the salient features of Big Data and how these
features impact on paradigm change on statistical and computational methods as
well as computing architectures. We also provide various new perspectives on
the Big Data analysis and computation. In particular, we emphasis on the
viability of the sparsest solution in high-confidence set and point out that
exogeneous assumptions in most statistical methods for Big Data can not be
validated due to incidental endogeneity. They can lead to wrong statistical
inferences and consequently wrong scientific conclusions
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