38 research outputs found
UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers
This paper proposes a novel approach for Simultaneous Localization and
Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches
use natural landmarks (such as keypoints). However, they are unstable over
time, repetitive in many cases or insufficient for a robust tracking (e.g. in
indoor buildings). On the other hand, other approaches have employed artificial
landmarks (such as squared fiducial markers) placed in the environment to help
tracking and relocalization. We propose a method that integrates both
approaches in order to achieve long-term robust tracking in many scenarios.
Our method has been compared to the start-of-the-art methods ORB-SLAM2 and
LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining better
precision, robustness and speed. Our tests also show that the combination of
markers and keypoints achieves better accuracy than each one of them
independently.Comment: Paper submitted to Pattern Recognitio
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
Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture
Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy.
Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones.
Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter.
The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one
Mixing body-parts model for 2D human pose estimation in stereo videos
This study targets 2D articulated human pose estimation (i.e. localisation of body limbs) in stereo videos. Although in recent years depth-based devices (e.g. Microsoft Kinect) have gained popularity, as they perform very well in controlled indoor environments (e.g. living rooms, operating theatres or gyms), they suffer clear problems in outdoor scenarios and, therefore, human pose estimation is still an interesting unsolved problem. The authors propose here a novel approach that is able to localise upper-body keypoints (i.e. shoulders, elbows, and wrists) in temporal sequences of stereo image pairs. The authors' method starts by locating and segmenting people in the image pairs by using disparity and appearance information. Then, a set of candidate body poses is computed for each view independently. Finally, temporal and stereo consistency is applied to estimate a final 2D pose. The authors' validate their model on three challenging datasets: `stereo human pose estimation dataset', `poses in the wild' and `INRIA 3DMovie'. The experimental results show that the authors' model not only establishes new state-of-the-art results on stereo sequences, but also brings improvements in monocular sequences
Parallelization Strategies for Markerless Human Motion Capture
Markerless Motion Capture (MMOCAP) is the
problem of determining the pose of a person from images
captured by one or several cameras simultaneously without
using markers on the subject. Evaluation of the solutions
is frequently the most time-consuming task, making most
of the proposed methods inapplicable in real-time scenarios.
This paper presents an efficient approach to parallelize
the evaluation of the solutions in CPUs and GPUs. Our proposal
is experimentally compared on six sequences of the
HumanEva-I dataset using the CMAES algorithm. Multiple
algorithm’s configurations were tested to analyze the
best trade-off in regard to the accuracy and computing time.
The proposed methods obtain speedups of 8× in multi-core
CPUs, 30× in a single GPU and up to 110× using 4 GPU
Multi-view gait recognition on curved
Appearance changes due to viewing angle changes cause difficulties for most of the gait recognition methods. In this paper, we propose a new approach for multi-view recognition, which allows to recognize people walking on curved paths. The recognition is based on 3D angular analysis of the movement of the walking human. A coarse-to-fine gait signature represents local variations on the angular measurements along time. A Support Vector Machine is used for classifying, and a sliding temporal window for majority vote policy is used to smooth and reinforce the classification results. The proposed approach has been experimentally validated on the publicly available “Kyushu University 4D Gait Database”
Entropy Volumes for Viewpoint Independent Gait Recognition
Gait as biometrics has been widely used for
human identi cation. However, direction changes cause
di culties for most of the gait recognition systems, due
to appearance changes. This study presents an e cient
multi-view gait recognition method that allows curved
trajectories on completely unconstrained paths for in-
door environments. Our method is based on volumet-
ric reconstructions of humans, aligned along their way.
A new gait descriptor, termed as Gait Entropy Vol-
ume (GEnV), is also proposed. GEnV focuses on cap-
turing 3D dynamical information of walking humans
through the concept of entropy. Our approach does
not require the sequence to be split into gait cycles.
A GEnV based signature is computed on the basis of
the previous 3D gait volumes. Each signature is clas-
si ed by a Support Vector Machine, and a majority
voting policy is used to smooth and reinforce the clas-
si cations results. The proposed approach is experimen-
tally validated on the \AVA Multi-View Gait Dataset
(AVAMVG)" and on the \Kyushu University 4D Gait
Database (KY4D)". The results show that this new ap-
proach achieves promising results in the problem of gait
recognition on unconstrained paths
Stereo Pictorial Structure for 2D Articulated Human Pose Estimation
In this paper, we consider the problem of 2D human
pose estimation on stereo image pairs. In particular,
we aim at estimating the location, orientation and scale of
upper-body parts of people detected in stereo image pairs
from realistic stereo videos that can be found in the Internet.
To address this task, we propose a novel pictorial structure
model to exploit the stereo information included in such
stereo image pairs: the Stereo Pictorial Structure (SPS). To
validate our proposed model, we contribute a new annotated
dataset of stereo image pairs, the Stereo Human Pose Estimation
Dataset (SHPED), obtained from YouTube stereoscopic
video sequences, depicting people in challenging poses
and diverse indoor and outdoor scenarios. The experimental
results on SHPED indicates that SPS improves on state-ofthe-
art monocular models thanks to the appropriate use of
the stereo informatio