15,509 research outputs found
Histogram of Oriented Principal Components for Cross-View Action Recognition
Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which are viewpoint
dependent. In contrast, we directly process pointclouds for cross-view action
recognition from unknown and unseen views. We propose the Histogram of Oriented
Principal Components (HOPC) descriptor that is robust to noise, viewpoint,
scale and action speed variations. At a 3D point, HOPC is computed by
projecting the three scaled eigenvectors of the pointcloud within its local
spatio-temporal support volume onto the vertices of a regular dodecahedron.
HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D
pointcloud sequences so that view-invariant STK descriptors (or Local HOPC
descriptors) at these key locations only are used for action recognition. We
also propose a global descriptor computed from the normalized spatio-temporal
distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the
performance of our proposed descriptors against nine existing techniques on two
cross-view and three single-view human action recognition datasets. The
Experimental results show that our techniques provide significant improvement
over state-of-the-art methods
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
Recurrent Attention Models for Depth-Based Person Identification
We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning task, our model is based on a combination of
convolutional and recurrent neural networks with the goal of identifying small,
discriminative regions indicative of human identity. We demonstrate that our
model produces state-of-the-art results on several published datasets given
only depth images. We further study the robustness of our model towards
viewpoint, appearance, and volumetric changes. Finally, we share insights
gleaned from interpretable 2D, 3D, and 4D visualizations of our model's
spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201
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