14,370 research outputs found
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and
Pattern Recognition Workshops, 201
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
Multi-target tracking is an important problem in civilian and military
applications. This paper investigates multi-target tracking in distributed
sensor networks. Data association, which arises particularly in multi-object
scenarios, can be tackled by various solutions. We consider sequential Monte
Carlo implementations of the Probability Hypothesis Density (PHD) filter based
on random finite sets. This approach circumvents the data association issue by
jointly estimating all targets in the region of interest. To this end, we
develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized
version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their
performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)
metric, benchmarked against a distributed extension of the Posterior
Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an
existing distributed PHD Particle Filter. Furthermore, the robustness of the
proposed tracking algorithms against outliers and their performance with
respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure
Sensing motion using spectral and spatial analysis of WLAN RSSI
In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis
confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of
WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements
in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same
data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the
state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support
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