19 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
Vegetation attenuation measurements and modeling in plantations for wireless sensor network planning
The impact of spatial temporal averaging on the dynamic statistical properties of rain fields
Knowledge of the spatial-temporal variation of rain fields is required for the planning and optimization of wide area high frequency terrestrial and satellite communication networks. This paper presents data and a method for characterizing multi-resolutions statistical/dynamic parameters describing the spatial-temporal variation of rain fields across ocean climate in North- Western Europe. The data is derived from the NIMROD network of rain radars. The characterizing parameters include: (i) statistical distribution of point one-minute rainfall rates, (ii) spatial and temporal correlation function of rainfall rate and, (iii) the probability of rain/no-rain. The main contributions of this paper are the assessment of the impact of varying spatial and temporal integration lengths on these parameters, their dependencies on the integration volumes and area sizes, and the model for both temporal and spatial correlation parameters