757 research outputs found
Human gait recognition based on multiview gait sequences
Copyright © 2008 X. Huang and N. V. Boulgouris. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person. This paper investigates the case in which several views are available for gait recognition. It is shown that each view has unequal discrimination power and, therefore, should have unequal contribution in the recognition process. In order to exploit the availability of multiple views, several methods for the combination of the results that are obtained from the individual views are tested and evaluated. A novel approach for the combination of the results from several views is also proposed based on the relative importance of each view. The proposed approach generates superior results, compared to those obtained by using individual views or by using multiple views that are combined using other combination methods.European Commissio
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
3D human pose estimation from depth maps using a deep combination of poses
Many real-world applications require the estimation of human body joints for
higher-level tasks as, for example, human behaviour understanding. In recent
years, depth sensors have become a popular approach to obtain three-dimensional
information. The depth maps generated by these sensors provide information that
can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps
employing a Deep Learning approach. We propose a model, named Deep Depth Pose
(DDP), which receives a depth map containing a person and a set of predefined
3D prototype poses and returns the 3D position of the body joints of the
person. In particular, DDP is defined as a ConvNet that computes the specific
weights needed to linearly combine the prototypes for the given input. We have
thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which
respectively depict realistic and synthetic samples, defining a new
state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and
Image Representation
The AVA Multi-View Dataset for Gait Recognition
In this paper, we introduce a new multi-view dataset for
gait recognition. The dataset was recorded in an indoor scenario, using
six convergent cameras setup to produce multi-view videos, where each
video depicts a walking human. Each sequence contains at least 3 complete
gait cycles. The dataset contains videos of 20 walking persons with
a large variety of body size, who walk along straight and curved paths.
The multi-view videos have been processed to produce foreground silhouettes.
To validate our dataset, we have extended some appearance-based
2D gait recognition methods to work with 3D data, obtaining very encouraging
results. The dataset, as well as camera calibration information,
is freely available for research purpose
Towards Realistic Facial Expression Recognition
Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework to collect realistic facial expression dataset from the Web. By adopting an active learning based method to remove noisy images from text based image search results, the proposed approach minimizes the human efforts during the dataset construction and maximizes the scalability for future research. Various novel facial expression features are then proposed to address the challenges imposed by the newly collected dataset. Finally, a spectral embedding based feature fusion framework is presented to combine the proposed facial expression features to form a more descriptive representation. This thesis also systematically investigates how the number of frames of a facial expression sequence can affect the performance of facial expression recognition algorithms, since facial expression sequences may be captured under different frame rates in realistic scenarios. A facial expression keyframe selection method is proposed based on keypoint based frame representation. Comprehensive experiments have been performed to demonstrate the effectiveness of the presented methods
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