430 research outputs found
Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model
Non-recurrent traffic congestion (NRTC) usually brings unexpected delays to
commuters. Hence, it is critical to accurately detect and recognize the NRTC in
a real-time manner. The advancement of road traffic detectors and loop
detectors provides researchers with a large-scale multivariable
temporal-spatial traffic data, which allows the deep research on NRTC to be
conducted. However, it remains a challenging task to construct an analytical
framework through which the natural spatial-temporal structural properties of
multivariable traffic information can be effectively represented and exploited
to better understand and detect NRTC. In this paper, we present a novel
analytical training-free framework based on coupled scalable Bayesian robust
tensor factorization (Coupled SBRTF). The framework can couple multivariable
traffic data including traffic flow, road speed, and occupancy through sharing
a similar or the same sparse structure. And, it naturally captures the
high-dimensional spatial-temporal structural properties of traffic data by
tensor factorization. With its entries revealing the distribution and magnitude
of NRTC, the shared sparse structure of the framework compasses sufficiently
abundant information about NRTC. While the low-rank part of the framework,
expresses the distribution of general expected traffic condition as an
auxiliary product. Experimental results on real-world traffic data show that
the proposed method outperforms coupled Bayesian robust principal component
analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and
standard normal deviates (SND) in detecting NRTC. The proposed method performs
even better when only traffic data in weekdays are utilized, and hence can
provide more precise estimation of NRTC for daily commuters
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Mining massive spatio-temporal data can help a variety of real-world
applications such as city capacity planning, event management, and social
network analysis. The tensor representation can be used to capture the
correlation between space and time and simultaneously exploit the latent
structure of the spatial and temporal patterns in an unsupervised fashion.
However, the increasing volume of spatio-temporal data has made it
prohibitively expensive to store and analyze using tensor factorization.
In this paper, we propose SkeTenSmooth, a novel tensor factorization
framework that uses adaptive sampling to compress the tensor in a temporally
streaming fashion and preserves the underlying global structure. SkeTenSmooth
adaptively samples incoming tensor slices according to the detected data
dynamics. Thus, the sketches are more representative and informative of the
tensor dynamic patterns. In addition, we propose a robust tensor factorization
method that can deal with the sketched tensor and recover the original
patterns. Experiments on the New York City Yellow Taxi data show that
SkeTenSmooth greatly reduces the memory cost and outperforms random sampling
and fixed rate sampling method in terms of retaining the underlying patterns
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation
Effective management of urban traffic is important for any smart city
initiative. Therefore, the quality of the sensory traffic data is of paramount
importance. However, like any sensory data, urban traffic data are prone to
imperfections leading to missing measurements. In this paper, we focus on
inter-region traffic data completion. We model the inter-region traffic as a
spatiotemporal tensor that suffers from missing measurements. To recover the
missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach
that considers the urban and temporal aspects of the traffic. To derive the
urban characteristics, we divide the area of study into regions. Then, for each
region, we compute urban feature vectors inspired from biodiversity which are
used to compute the urban similarity matrix. To mine the temporal aspect, we
first conduct an entropy analysis to determine the most regular time-series.
Then, we conduct a joint Fourier and correlation analysis to compute its
periodicity and construct the temporal matrix. Both urban and temporal matrices
are fed into a modified CP-completion objective function. To solve this
objective, we propose an alternating least square approach that operates on the
vectorized version of the inputs. We conduct comprehensive comparative study
with two evaluation scenarios. In the first one, we simulate random missing
values. In the second scenario, we simulate missing values at a given area and
time duration. Our results demonstrate that our approach provides effective
recovering performance reaching 26% improvement compared to state-of-art CP
approaches and 35% compared to state-of-art generative model-based approaches
Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion
Tensor completion refers to the task of estimating the missing data from an
incomplete measurement or observation, which is a core problem frequently
arising from the areas of big data analysis, computer vision, and network
engineering. Due to the multidimensional nature of high-order tensors, the
matrix approaches, e.g., matrix factorization and direct matricization of
tensors, are often not ideal for tensor completion and recovery. Exploiting the
potential periodicity and inherent correlation properties appeared in
real-world tensor data, in this paper, we shall incorporate the low-rank and
sparse regularization technique to enhance Tucker decomposition for tensor
completion. A series of computational experiments on real-world datasets,
including internet traffic data, color images, and face recognition, show that
our model performs better than many existing state-of-the-art matricization and
tensorization approaches in terms of achieving higher recovery accuracy.Comment: 14 pages and 14 figures and 1 tabl
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks
Short-term road traffic prediction (STTP) is one of the most important
modules in Intelligent Transportation Systems (ITS). However, network-level
STTP still remains challenging due to the difficulties both in modeling the
diverse traffic patterns and tacking high-dimensional time series with low
latency. Therefore, a framework combining with a deep clustering (DeepCluster)
module is developed for STTP at largescale networks in this paper. The
DeepCluster module is proposed to supervise the representation learning in a
visualized way from the large unlabeled dataset. More specifically, to fully
exploit the traffic periodicity, the raw series is first split into a number of
sub-series for triplets generation. The convolutional neural networks (CNNs)
with triplet loss are utilized to extract the features of shape by transferring
the series into visual images. The shape-based representations are then used
for road segments clustering. Thereafter, motivated by the fact that the road
segments in a group have similar patterns, a model sharing strategy is further
proposed to build recurrent NNs (RNNs)-based predictions through a group-based
model (GM), instead of individual-based model (IM) in which one model are built
for one road exclusively. Our framework can not only significantly reduce the
number of models and cost, but also increase the number of training data and
the diversity of samples. In the end, we evaluate the proposed framework over
the network of Liuli Bridge in Beijing. Experimental results show that the
DeepCluster can effectively cluster the road segments and GM can achieve
comparable performance against the IM with less number of models.Comment: 12 pages, 15 figures, journa
Network Anomaly Detection based on Tensor Decomposition
The problem of detecting anomalies in time series from network measurements
has been widely studied and is a topic of fundamental importance. Many anomaly
detection methods are based on packet inspection collected at the network core
routers, with consequent disadvantages in terms of computational cost and
privacy. We propose an alternative method in which packet header inspection is
not needed. The method is based on the extraction of a normal subspace obtained
by the tensor decomposition technique considering the correlation between
different metrics. We propose a new approach for online tensor decomposition
where changes in the normal subspace can be tracked efficiently. Another
advantage of our proposal is the interpretability of the obtained models. The
flexibility of the method is illustrated by applying it to two distinct
examples, both using actual data collected on residential routers
Detailed 2D-3D Joint Representation for Human-Object Interaction
Human-Object Interaction (HOI) detection lies at the core of action
understanding. Besides 2D information such as human/object appearance and
locations, 3D pose is also usually utilized in HOI learning since its
view-independence. However, rough 3D body joints just carry sparse body
information and are not sufficient to understand complex interactions. Thus, we
need detailed 3D body shape to go further. Meanwhile, the interacted object in
3D is also not fully studied in HOI learning. In light of these, we propose a
detailed 2D-3D joint representation learning method. First, we utilize the
single-view human body capture method to obtain detailed 3D body, face and hand
shapes. Next, we estimate the 3D object location and size with reference to the
2D human-object spatial configuration and object category priors. Finally, a
joint learning framework and cross-modal consistency tasks are proposed to
learn the joint HOI representation. To better evaluate the 2D ambiguity
processing capacity of models, we propose a new benchmark named Ambiguous-HOI
consisting of hard ambiguous images. Extensive experiments in large-scale HOI
benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code
and data are available at https://github.com/DirtyHarryLYL/DJ-RN.Comment: Accepted to CVPR 2020, supplementary materials included, code
available:https://github.com/DirtyHarryLYL/DJ-R
Network of Tensor Time Series
Co-evolving time series appears in a multitude of applications such as
environmental monitoring, financial analysis, and smart transportation. This
paper aims to address the following challenges, including (C1) how to
incorporate explicit relationship networks of the time series; (C2) how to
model the implicit relationship of the temporal dynamics. We propose a novel
model called Network of Tensor Time Series, which is comprised of two modules,
including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural
Network (TRNN). TGCN tackles the first challenge by generalizing Graph
Convolutional Network (GCN) for flat graphs to tensor graphs, which captures
the synergy between multiple graphs associated with the tensors. TRNN leverages
tensor decomposition to model the implicit relationships among co-evolving time
series. The experimental results on five real-world datasets demonstrate the
efficacy of the proposed method.Comment: Accepted by WWW'202
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
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