8,455 research outputs found
Human mobility monitoring in very low resolution visual sensor network
This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics
First prototype of a silicon tracker using an artificial retina for fast track finding
We report on the R\&D for a first prototype of a silicon tracker based on an
alternative approach for fast track finding. The working principle is inspired
from neurobiology, in particular by the processing of visual images by the
brain as it happens in nature. It is based on extensive parallelisation of data
distribution and pattern recognition. In this work we present the design of a
practical device that consists of a telescope based on single-sided silicon
detectors; we describe the data acquisition system and the implementation of
the track finding algorithms using available digital logic of commercial FPGA
devices. Tracking performance and trigger capabilities of the device are
discussed along with perspectives for future applications.Comment: 9 pages, 7 figures, Technology and Instrumentation in Particle
Physics 2014 (TIPP 2014), conference proceeding
Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
In this work, we address the problem of cross-modal comparison of aerial data
streams. A variety of simulated automobile trajectories are sensed using two
different modalities: full-motion video, and radio-frequency (RF) signals
received by detectors at various locations. The information represented by the
two modalities is compared using self-similarity matrices (SSMs) corresponding
to time-ordered point clouds in feature spaces of each of these data sources;
we note that these feature spaces can be of entirely different scale and
dimensionality. Several metrics for comparing SSMs are explored, including a
cutting-edge time-warping technique that can simultaneously handle local time
warping and partial matches, while also controlling for the change in geometry
between feature spaces of the two modalities. We note that this technique is
quite general, and does not depend on the choice of modalities. In this
particular setting, we demonstrate that the cross-modal distance between SSMs
corresponding to the same trajectory type is smaller than the cross-modal
distance between SSMs corresponding to distinct trajectory types, and we
formalize this observation via precision-recall metrics in experiments.
Finally, we comment on promising implications of these ideas for future
integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201
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