4,914 research outputs found
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available
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
- …