14,182 research outputs found
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
Differential Recurrent Neural Networks for Action Recognition
The long short-term memory (LSTM) neural network is capable of processing
complex sequential information since it utilizes special gating schemes for
learning representations from long input sequences. It has the potential to
model any sequential time-series data, where the current hidden state has to be
considered in the context of the past hidden states. This property makes LSTM
an ideal choice to learn the complex dynamics of various actions.
Unfortunately, the conventional LSTMs do not consider the impact of
spatio-temporal dynamics corresponding to the given salient motion patterns,
when they gate the information that ought to be memorized through time. To
address this problem, we propose a differential gating scheme for the LSTM
neural network, which emphasizes on the change in information gain caused by
the salient motions between the successive frames. This change in information
gain is quantified by Derivative of States (DoS), and thus the proposed LSTM
model is termed as differential Recurrent Neural Network (dRNN). We demonstrate
the effectiveness of the proposed model by automatically recognizing actions
from the real-world 2D and 3D human action datasets. Our study is one of the
first works towards demonstrating the potential of learning complex time-series
representations via high-order derivatives of states
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