16,446 research outputs found
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
STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
Human mobility forecasting in a city is of utmost importance to
transportation and public safety, but with the process of urbanization and the
generation of big data, intensive computing and determination of mobility
pattern have become challenging. This study focuses on how to improve the
accuracy and efficiency of predicting citywide human mobility via a simpler
solution. A spatio-temporal mobility event prediction framework based on a
single fully-convolutional residual network (STAR) is proposed. STAR is a
highly simple, general and effective method for learning a single tensor
representing the mobility event. Residual learning is utilized for training the
deep network to derive the detailed result for scenarios of citywide
prediction. Extensive benchmark evaluation results on real-world data
demonstrate that STAR outperforms state-of-the-art approaches in single- and
multi-step prediction while utilizing fewer parameters and achieving higher
efficiency.Comment: Accepted by MDM 201
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