1,762 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
Measuring Membership Privacy on Aggregate Location Time-Series
While location data is extremely valuable for various applications,
disclosing it prompts serious threats to individuals' privacy. To limit such
concerns, organizations often provide analysts with aggregate time-series that
indicate, e.g., how many people are in a location at a time interval, rather
than raw individual traces. In this paper, we perform a measurement study to
understand Membership Inference Attacks (MIAs) on aggregate location
time-series, where an adversary tries to infer whether a specific user
contributed to the aggregates.
We find that the volume of contributed data, as well as the regularity and
particularity of users' mobility patterns, play a crucial role in the attack's
success. We experiment with a wide range of defenses based on generalization,
hiding, and perturbation, and evaluate their ability to thwart the attack
vis-a-vis the utility loss they introduce for various mobility analytics tasks.
Our results show that some defenses fail across the board, while others work
for specific tasks on aggregate location time-series. For instance, suppressing
small counts can be used for ranking hotspots, data generalization for
forecasting traffic, hotspot discovery, and map inference, while sampling is
effective for location labeling and anomaly detection when the dataset is
sparse. Differentially private techniques provide reasonable accuracy only in
very specific settings, e.g., discovering hotspots and forecasting their
traffic, and more so when using weaker privacy notions like crowd-blending
privacy. Overall, our measurements show that there does not exist a unique
generic defense that can preserve the utility of the analytics for arbitrary
applications, and provide useful insights regarding the disclosure of sanitized
aggregate location time-series
Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture
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