50,760 research outputs found
Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
In this paper, we investigate the problem of nonmyopic (multi-step ahead)
quantizer design for target tracking using a wireless sensor network. Adopting
the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the
optimization metric, we theoretically show that this problem can be temporally
decomposed over a certain time window. Based on sequential Monte-Carlo methods
for tracking, i.e., particle filters, we design the local quantizer adaptively
by solving a particlebased non-linear optimization problem which is well suited
for the use of interior-point algorithm and easily embedded in the filtering
process. Simulation results are provided to illustrate the effectiveness of our
proposed approach.Comment: Submitted to 2013 Asilomar Conference on Signals, Systems, and
Computer
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
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