1,810 research outputs found
Physical simulation for monocular 3D model based tracking
The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle filter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle filter tracker, with the force noise method producing the best results over the range of test videos
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 8Ă— in multi-core
CPUs, 30Ă— in a single GPU and up to 110Ă— using 4 GPU
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Recent advances in monocular model-based tracking: a systematic literature review
In this paper, we review the advances of monocular model-based tracking for
last ten years period until 2014. In 2005, Lepetit, et. al, [19] reviewed the status
of monocular model based rigid body tracking. Since then, direct 3D tracking has
become quite popular research area, but monocular model-based tracking should
still not be forgotten. We mainly focus on tracking, which could be applied to aug-
mented reality, but also some other applications are covered. Given the wide subject
area this paper tries to give a broad view on the research that has been conducted,
giving the reader an introduction to the different disciplines that are tightly related
to model-based tracking. The work has been conducted by searching through well
known academic search databases in a systematic manner, and by selecting certain
publications for closer examination. We analyze the results by dividing the found
papers into different categories by their way of implementation. The issues which
have not yet been solved are discussed. We also discuss on emerging model-based
methods such as fusing different types of features and region-based pose estimation
which could show the way for future research in this subject.Siirretty Doriast
PPF - A Parallel Particle Filtering Library
We present the parallel particle filtering (PPF) software library, which
enables hybrid shared-memory/distributed-memory parallelization of particle
filtering (PF) algorithms combining the Message Passing Interface (MPI) with
multithreading for multi-level parallelism. The library is implemented in Java
and relies on OpenMPI's Java bindings for inter-process communication. It
includes dynamic load balancing, multi-thread balancing, and several
algorithmic improvements for PF, such as input-space domain decomposition. The
PPF library hides the difficulties of efficient parallel programming of PF
algorithms and provides application developers with the necessary tools for
parallel implementation of PF methods. We demonstrate the capabilities of the
PPF library using two distributed PF algorithms in two scenarios with different
numbers of particles. The PPF library runs a 38 million particle problem,
corresponding to more than 1.86 GB of particle data, on 192 cores with 67%
parallel efficiency. To the best of our knowledge, the PPF library is the first
open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data
Fusion & Target Tracking Conference 201
A fast framework construction and visualization method for particle-based fluid
© 2017, The Author(s). Fast and vivid fluid simulation and visualization is a challenge topic of study in recent years. Particle-based simulation method has been widely used in the art animation modeling and multimedia field. However, the requirements of huge numerical calculation and high quality of visualization usually result in a poor computing efficiency. In this work, in order to improve those issues, we present a fast framework for 3D fluid fast constructing and visualization which parallelizes the fluid algorithm based on the GPU computing framework and designs a direct surface visualization method for particle-based fluid data such as WCSPH, IISPH, and PCISPH. Considering on conventional polygonization or adaptive mesh methods may incur high computing costs and detail losses, an improved particle-based method is provided for real-time fluid surface rendering with the screen-space technology and the utilities of the modern graphics hardware to achieve the high performance rendering; meanwhile, it effectively protects fluid details. Furthermore, to realize the fast construction of scenes, an optimized design of parallel framework and interface is also discussed in our paper. Our method is convenient to enforce, and the results demonstrate a significant improvement in the performance and efficiency by being compared with several examples
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