3,403 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Context Exploitation in Data Fusion
Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics.
Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context.
We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Activity understanding and unusual event detection in surveillance videos
PhDComputer scientists have made ceaseless efforts to replicate cognitive video understanding abilities
of human brains onto autonomous vision systems. As video surveillance cameras become
ubiquitous, there is a surge in studies on automated activity understanding and unusual event detection
in surveillance videos. Nevertheless, video content analysis in public scenes remained a
formidable challenge due to intrinsic difficulties such as severe inter-object occlusion in crowded
scene and poor quality of recorded surveillance footage. Moreover, it is nontrivial to achieve
robust detection of unusual events, which are rare, ambiguous, and easily confused with noise.
This thesis proposes solutions for resolving ambiguous visual observations and overcoming unreliability
of conventional activity analysis methods by exploiting multi-camera visual context
and human feedback.
The thesis first demonstrates the importance of learning visual context for establishing reliable
reasoning on observed activity in a camera network. In the proposed approach, a new Cross
Canonical Correlation Analysis (xCCA) is formulated to discover and quantify time delayed pairwise
correlations of regional activities observed within and across multiple camera views. This
thesis shows that learning time delayed pairwise activity correlations offers valuable contextual
information for (1) spatial and temporal topology inference of a camera network, (2) robust person
re-identification, and (3) accurate activity-based video temporal segmentation. Crucially, in
contrast to conventional methods, the proposed approach does not rely on either intra-camera or
inter-camera object tracking; it can thus be applied to low-quality surveillance videos featuring
severe inter-object occlusions.
Second, to detect global unusual event across multiple disjoint cameras, this thesis extends
visual context learning from pairwise relationship to global time delayed dependency between
regional activities. Specifically, a Time Delayed Probabilistic Graphical Model (TD-PGM) is
proposed to model the multi-camera activities and their dependencies. Subtle global unusual
events are detected and localised using the model as context-incoherent patterns across multiple
camera views. In the model, different nodes represent activities in different decomposed re3
gions from different camera views, and the directed links between nodes encoding time delayed
dependencies between activities observed within and across camera views. In order to learn optimised
time delayed dependencies in a TD-PGM, a novel two-stage structure learning approach
is formulated by combining both constraint-based and scored-searching based structure learning
methods.
Third, to cope with visual context changes over time, this two-stage structure learning approach
is extended to permit tractable incremental update of both TD-PGM parameters and its
structure. As opposed to most existing studies that assume static model once learned, the proposed
incremental learning allows a model to adapt itself to reflect the changes in the current
visual context, such as subtle behaviour drift over time or removal/addition of cameras. Importantly,
the incremental structure learning is achieved without either exhaustive search in a large
graph structure space or storing all past observations in memory, making the proposed solution
memory and time efficient.
Forth, an active learning approach is presented to incorporate human feedback for on-line
unusual event detection. Contrary to most existing unsupervised methods that perform passive
mining for unusual events, the proposed approach automatically requests supervision for critical
points to resolve ambiguities of interest, leading to more robust detection of subtle unusual
events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision
on-the-fly on whether to request label for each unlabelled sample observed in sequence.
It selects adaptively two active learning criteria, namely likelihood criterion and uncertainty criterion
to achieve (1) discovery of unknown event classes and (2) refinement of classification
boundary.
The effectiveness of the proposed approaches is validated using videos captured from busy
public scenes such as underground stations and traffic intersections
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