176,128 research outputs found
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition
The ubiquitous availability of smartphones and smartwatches with integrated
inertial measurement units (IMUs) enables straightforward capturing of human
activities. For specific applications of sensor based human activity
recognition (HAR), however, logistical challenges and burgeoning costs render
especially the ground truth annotation of such data a difficult endeavor,
resulting in limited scale and diversity of datasets. Transfer learning, i.e.,
leveraging publicly available labeled datasets to first learn useful
representations that can then be fine-tuned using limited amounts of labeled
data from a target domain, can alleviate some of the performance issues of
contemporary HAR systems. Yet they can fail when the differences between source
and target conditions are too large and/ or only few samples from a target
application domain are available, each of which are typical challenges in
real-world human activity recognition scenarios. In this paper, we present an
approach for economic use of publicly available labeled HAR datasets for
effective transfer learning. We introduce a novel transfer learning framework,
Cross-Domain HAR, which follows the teacher-student self-training paradigm to
more effectively recognize activities with very limited label information. It
bridges conceptual gaps between source and target domains, including sensor
locations and type of activities. Through our extensive experimental evaluation
on a range of benchmark datasets, we demonstrate the effectiveness of our
approach for practically relevant few shot activity recognition scenarios. We
also present a detailed analysis into how the individual components of our
framework affect downstream performance
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
Many of the state-of-the-art algorithms for gesture recognition are based on
Conditional Random Fields (CRFs). Successful approaches, such as the
Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose
values are mapped to the values of the labels. In this paper we propose a novel
methodology to set the latent values according to the gesture complexity. We
use an heuristic that iterates through the samples associated with each label
value, stimating their complexity. We then use it to assign the latent values
to the label values. We evaluate our method on the task of recognizing human
gestures from video streams. The experiments were performed in binary datasets,
generated by grouping different labels. Our results demonstrate that our
approach outperforms the arbitrary one in many cases, increasing the accuracy
by up to 10%.Comment: Conference paper published at 2016 29th SIBGRAPI, Conference on
Graphics, Patterns and Images (SIBGRAPI). 8 pages, 7 figure
GART: The Gesture and Activity Recognition Toolkit
Presented at the 12th International Conference on Human-Computer Interaction, Beijing, China, July 2007.The original publication is available at www.springerlink.comThe Gesture and Activity Recognition Toolit (GART) is
a user interface toolkit designed to enable the development of gesture-based
applications. GART provides an abstraction to machine learning
algorithms suitable for modeling and recognizing different types of
gestures. The toolkit also provides support for the data collection and
the training process. In this paper, we present GART and its machine
learning abstractions. Furthermore, we detail the components of the
toolkit and present two example gesture recognition applications
- …