3,174 research outputs found
Ego-Downward and Ambient Video based Person Location Association
Using an ego-centric camera to do localization and tracking is highly needed
for urban navigation and indoor assistive system when GPS is not available or
not accurate enough. The traditional hand-designed feature tracking and
estimation approach would fail without visible features. Recently, there are
several works exploring to use context features to do localization. However,
all of these suffer severe accuracy loss if given no visual context
information. To provide a possible solution to this problem, this paper
proposes a camera system with both ego-downward and third-static view to
perform localization and tracking in a learning approach. Besides, we also
proposed a novel action and motion verification model for cross-view
verification and localization. We performed comparative experiments based on
our collected dataset which considers the same dressing, gender, and background
diversity. Results indicate that the proposed model can achieve
improvement in accuracy performance. Eventually, we tested the model on
multi-people scenarios and obtained an average accuracy
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
Radar-only ego-motion estimation in difficult settings via graph matching
Radar detects stable, long-range objects under variable weather and lighting
conditions, making it a reliable and versatile sensor well suited for
ego-motion estimation. In this work, we propose a radar-only odometry pipeline
that is highly robust to radar artifacts (e.g., speckle noise and false
positives) and requires only one input parameter. We demonstrate its ability to
adapt across diverse settings, from urban UK to off-road Iceland, achieving a
scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS
as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We
present algorithms for keypoint extraction and data association, framing the
latter as a graph matching optimization problem, and provide an in-depth system
analysis.Comment: 6 content pages, 1 page of references, 5 figures, 4 tables, 2019 IEEE
International Conference on Robotics and Automation (ICRA
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