287,445 research outputs found
A Survey of Deep Learning Techniques for Mobile Robot Applications
Advancements in deep learning over the years have attracted research into how
deep artificial neural networks can be used in robotic systems. This research
survey will present a summarization of the current research with a specific
focus on the gains and obstacles for deep learning to be applied to mobile
robotics
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
Deep learning has recently achieved very promising results in a wide range of
areas such as computer vision, speech recognition and natural language
processing. It aims to learn hierarchical representations of data by using deep
architecture models. In a smart city, a lot of data (e.g. videos captured from
many distributed sensors) need to be automatically processed and analyzed. In
this paper, we review the deep learning algorithms applied to video analytics
of smart city in terms of different research topics: object detection, object
tracking, face recognition, image classification and scene labeling.Comment: 8 pages, 18 figure
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
Domain Generalization via Universal Non-volume Preserving Models
Recognition across domains has recently become an active topic in the
research community. However, it has been largely overlooked in the problem of
recognition in new unseen domains. Under this condition, the delivered deep
network models are unable to be updated, adapted, or fine-tuned. Therefore,
recent deep learning techniques, such as domain adaptation, feature
transferring, and fine-tuning, cannot be applied. This paper presents a novel
approach to the problem of domain generalization in the context of deep
learning. The proposed method is evaluated on different datasets in various
problems, i.e. (i) digit recognition on MNIST, SVHN, and MNIST-M, (ii) face
recognition on Extended Yale-B, CMU-PIE and CMU-MPIE, and (iii) pedestrian
recognition on RGB and Thermal image datasets. The experimental results show
that our proposed method consistently improves performance accuracy. It can
also be easily incorporated with any other CNN frameworks within an end-to-end
deep network design for object detection and recognition problems to improve
their performance.Comment: Accepted to Computer and Robot Vision 2020. arXiv admin note:
substantial text overlap with arXiv:1812.0340
Multi-velocity neural networks for gesture recognition in videos
We present a new action recognition deep neural network which adaptively
learns the best action velocities in addition to the classification. While deep
neural networks have reached maturity for image understanding tasks, we are
still exploring network topologies and features to handle the richer
environment of video clips. Here, we tackle the problem of multiple velocities
in action recognition, and provide state-of-the-art results for gesture
recognition, on known and new collected datasets. We further provide the
training steps for our semi-supervised network, suited to learn from huge
unlabeled datasets with only a fraction of labeled examples
A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics
Facial micro-expressions are very brief, spontaneous facial expressions that
appear on the face of humans when they either deliberately or unconsciously
conceal an emotion. Micro-expression has shorter duration than
macro-expression, which makes it more challenging for human and machine. Over
the past ten years, automatic micro-expressions recognition has attracted
increasing attention from researchers in psychology, computer science,
security, neuroscience and other related disciplines. The aim of this paper is
to provide the insights of automatic micro-expressions and recommendations for
future research. There has been a lot of datasets released over the last decade
that facilitated the rapid growth in this field. However, comparison across
different datasets is difficult due to the inconsistency in experiment
protocol, features used and evaluation methods. To address these issues, we
review the datasets, features and the performance metrics deployed in the
literature. Relevant challenges such as the spatial temporal settings during
data collection, emotional classes versus objective classes in data labelling,
face regions in data analysis, standardisation of metrics and the requirements
for real-world implementation are discussed. We conclude by proposing some
promising future directions to advancing micro-expressions research.Comment: Preprint submitted to IEEE Transaction
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Mobile biometric approaches provide the convenience of secure authentication
with an omnipresent technology. However, this brings an additional challenge of
recognizing biometric patterns in unconstrained environment including
variations in mobile camera sensors, illumination conditions, and capture
distance. To address the heterogeneous challenge, this research presents a
novel heterogeneity aware loss function within a deep learning framework. The
effectiveness of the proposed loss function is evaluated for periocular
biometrics using the CSIP, IMP and VISOB mobile periocular databases. The
results show that the proposed algorithm yields state-of-the-art results in a
heterogeneous environment and improves generalizability for cross-database
experiments
Recent Advances in Open Set Recognition: A Survey
In real-world recognition/classification tasks, limited by various objective
factors, it is usually difficult to collect training samples to exhaust all
classes when training a recognizer or classifier. A more realistic scenario is
open set recognition (OSR), where incomplete knowledge of the world exists at
training time, and unknown classes can be submitted to an algorithm during
testing, requiring the classifiers to not only accurately classify the seen
classes, but also effectively deal with the unseen ones. This paper provides a
comprehensive survey of existing open set recognition techniques covering
various aspects ranging from related definitions, representations of models,
datasets, evaluation criteria, and algorithm comparisons. Furthermore, we
briefly analyze the relationships between OSR and its related tasks including
zero-shot, one-shot (few-shot) recognition/learning techniques, classification
with reject option, and so forth. Additionally, we also overview the open world
recognition which can be seen as a natural extension of OSR. Importantly, we
highlight the limitations of existing approaches and point out some promising
subsequent research directions in this field.Comment: Accepted by IEEE TPAM
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive
discussion and exploration about how low-level vision techniques can benefit
the high-level automatic visual recognition in various scenarios. In its second
track, we focus on object or face detection in poor visibility enhancements
caused by bad weathers (haze, rain) and low light conditions. While existing
enhancement methods are empirically expected to help the high-level end task,
that is observed to not always be the case in practice. To provide a more
thorough examination and fair comparison, we introduce three benchmark sets
collected in real-world hazy, rainy, and low-light conditions, respectively,
with annotate objects/faces annotated. To our best knowledge, this is the first
and currently largest effort of its kind. Baseline results by cascading
existing enhancement and detection models are reported, indicating the highly
challenging nature of our new data as well as the large room for further
technical innovations. We expect a large participation from the broad research
community to address these challenges together.Comment: A summary paper on datasets, fact sheets, baseline results, challenge
results, and winning methods in UG Challenge (Track 2). More materials
are provided in http://www.ug2challenge.org/index.htm
Unique Identification of Macaques for Population Monitoring and Control
Despite loss of natural habitat due to development and urbanization, certain
species like the Rhesus macaque have adapted well to the urban environment.
With abundant food and no predators, macaque populations have increased
substantially in urban areas, leading to frequent conflicts with humans.
Overpopulated areas often witness macaques raiding crops, feeding on bird and
snake eggs as well as destruction of nests, thus adversely affecting other
species in the ecosystem. In order to mitigate these adverse effects,
sterilization has emerged as a humane and effective way of population control
of macaques. As sterilization requires physical capture of individuals or
groups, their unique identification is integral to such control measures. In
this work, we propose the Macaque Face Identification (MFID), an image based,
non-invasive tool that relies on macaque facial recognition to identify
individuals, and can be used to verify if they are sterilized. Our primary
contribution is a robust facial recognition and verification module designed
for Rhesus macaques, but extensible to other non-human primate species. We
evaluate the performance of MFID on a dataset of 93 monkeys under closed set,
open set and verification evaluation protocols. Finally, we also report state
of the art results when evaluating our proposed model on endangered primate
species
- …