104 research outputs found
Optimized Packet Scheduling in Multiview Video Navigation Systems
In multiview video systems, multiple cameras generally acquire the same scene
from different perspectives, such that users have the possibility to select
their preferred viewpoint. This results in large amounts of highly redundant
data, which needs to be properly handled during encoding and transmission over
resource-constrained channels. In this work, we study coding and transmission
strategies in multicamera systems, where correlated sources send data through a
bottleneck channel to a central server, which eventually transmits views to
different interactive users. We propose a dynamic correlation-aware packet
scheduling optimization under delay, bandwidth, and interactivity constraints.
The optimization relies both on a novel rate-distortion model, which captures
the importance of each view in the 3D scene reconstruction, and on an objective
function that optimizes resources based on a client navigation model. The
latter takes into account the distortion experienced by interactive clients as
well as the distortion variations that might be observed by clients during
multiview navigation. We solve the scheduling problem with a novel
trellis-based solution, which permits to formally decompose the multivariate
optimization problem thereby significantly reducing the computation complexity.
Simulation results show the gain of the proposed algorithm compared to baseline
scheduling policies. More in details, we show the gain offered by our dynamic
scheduling policy compared to static camera allocation strategies and to
schemes with constant coding strategies. Finally, we show that the best
scheduling policy consistently adapts to the most likely user navigation path
and that it minimizes distortion variations that can be very disturbing for
users in traditional navigation systems
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
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