526 research outputs found
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
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement
Volume measurement plays an important role in the production and processing of food products. Various methods have been
proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction
comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction
have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs
volume measurements using random points. Monte Carlo method only requires information regarding whether random points
fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a
computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with
heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images.
Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from
binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the
water displacement method. In addition, the proposed method is more accurate and faster than the space carving method
Mobile Robots
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations
Combining Differential Kinematics and Optical Flow for Automatic Labeling of Continuum Robots in Minimally Invasive Surgery
International audienceThe segmentation of continuum robots in medical images can be of interest for analyzing surgical procedures or for controlling them. However, the automatic segmentation of continuous and flexible shapes is not an easy task. On one hand conventional approaches are not adapted to the specificities of these instruments, such as imprecise kinematic models, and on the other hand techniques based on deep-learning showed interesting capabilities but need many manually labeled images. In this article we propose a novel approach for segmenting continuum robots on endoscopic images, which requires no prior on the instrument visual appearance and no manual annotation of images. The method relies on the use of the combination of kinematic models and differential kinematic models of the robot and the analysis of optical flow in the images. A cost function aggregating information from the acquired image, from optical flow and from robot encoders is optimized using particle swarm optimization and provides estimated parameters of the pose of the continuum instrument and a mask defining the instrument in the image. In addition a temporal consistency is assessed in order to improve stochastic optimization and reject outliers. The proposed approach has been tested for the robotic instruments of a flexible endoscopy platform both for benchtop acquisitions and an in vivo video. The results show the ability of the technique to correctly segment the instruments without a prior, and in challenging conditions. The obtained segmentation can be used for several applications, for instance for providing automatic labels for machine learning techniques
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
Physical Interaction of Autonomous Robots in Complex Environments
Recent breakthroughs in the fields of computer vision and robotics are firmly changing the people perception about robots. The idea of robots that substitute humansisnowturningintorobotsthatcollaboratewiththem. Serviceroboticsconsidersrobotsaspersonalassistants. Itsafelyplacesrobotsindomesticenvironments in order to facilitate humans daily life. Industrial robotics is now reconsidering its basic idea of robot as a worker. Currently, the primary method to guarantee the personnels safety in industrial environments is the installation of physical barriers around the working area of robots. The development of new technologies and new algorithms in the sensor field and in the robotic one has led to a new generation of lightweight and collaborative robots. Therefore, industrial robotics leveraged the intrinsic properties of this kind of robots to generate a robot co-worker that is able to safely coexist, collaborate and interact inside its workspace with both personnels and objects. This Ph.D. dissertation focuses on the generation of a pipeline for fast object pose estimation and distance computation of moving objects,in both structured and unstructured environments,using RGB-D images. This pipeline outputs the command actions which let the robot complete its main task and fulfil the safety human-robot coexistence behaviour at once. The proposed pipeline is divided into an object segmentation part,a 6D.o.F. object pose estimation part and a real-time collision avoidance part for safe human-robot coexistence. Firstly, the segmentation module finds candidate object clusters out of RGB-D images of clutter scenes using a graph-based image segmentation technique. This segmentation technique generates a cluster of pixels for each object found in the image. The candidate object clusters are then fed as input to the 6 D.o.F. object pose estimation module. The latter is in charge of estimating both the translation and the orientation in 3D space of each candidate object clusters. The object pose is then employed by the robotic arm to compute a suitable grasping policy. The last module generates a force vector field of the environment surrounding the robot, the objects and the humans. This force vector field drives the robot toward its goal while any potential collision against objects and/or humans is safely avoided. This work has been carried out at Politecnico di Torino, in collaboration with Telecom Italia S.p.A
Accelerated Object Tracking with Local Binary Features
Multi-object tracking is a problem with wide application in modern computing. Object tracking is leveraged in areas such as human computer interaction, autonomous vehicle navigation, panorama generation, as well as countless other robotic applications. Several trackers have demonstrated favorable results for tracking of single objects. However, modern object trackers must make significant tradeoffs in order to accommodate multiple objects while maintaining real-time performance. These tradeoffs include sacrifices in robustness and accuracy that adversely affect the results.
This thesis details the design and multiple implementations of an object tracker that is focused on computational efficiency. The computational efficiency of the tracker is achieved through use of local binary descriptors in a template matching approach. Candidate templates are matched to a dictionary composed of both static and dynamic templates to allow for variation in the appearance of the object while minimizing the potential for drift in the tracker. Locality constraints have been used to reduce tracking jitter. Due to the significant promise for parallelization, the tracking algorithm was implemented on the Graphics Processing Unit (GPU) using the CUDA API. The tracker\u27s efficiency also led to its implantation on a mobile platform as one of the mobile trackers that can accurately track at faster than realtime speed. Benchmarks were performed to compare the proposed tracker to state of the art trackers on a wide range of standard test videos. The tracker implemented in this work has demonstrated a higher degree of accuracy while operating several orders of magnitude faster
Articulation estimation and real-time tracking of human hand motions
Schröder M. Articulation estimation and real-time tracking of human hand motions. Bielefeld: Universität Bielefeld; 2015.This thesis deals with the problem of estimating and tracking the full articulation of
human hands. Algorithmically recovering hand articulations is a challenging problem
due to the hand’s high number of degrees of freedom and the complexity of its
motions. Besides the accuracy and efficiency of the hand posture estimation, hand
tracking methods are faced with issues such as invasiveness, ease of deployment
and sensor artifacts. In this thesis several different hand tracking approaches are examined,
including marker-based optical motion capture, data-driven discriminative
visual tracking and generative tracking based on articulated registration, and various
contributions to these areas are presented. The problem of optimally placing reduced
marker sets on a performer’s hand for optical hand motion capture is explored. A
method is proposed that automatically generates functional reduced marker layouts
by optimizing for their numerical stability and geometric feasibility. A data-driven
discriminative tracking approach based on matching the hand’s appearance in the
sensor data with an image database is investigated. In addition to an efficient nearest
neighbor search for images, a combination of discriminative initialization and
generative refinement is employed. The method’s applicability is demonstrated in
interactive robot teleoperation. Various real human hand motions are captured and
statistically analyzed to derive low-dimensional representations of hand articulations.
An adaptive hand posture subspace concept is developed and integrated into a generative
real-time hand tracking approach that aligns a virtual hand model with sensor
point clouds based on constrained inverse kinematics. Generative hand tracking is
formulated as a regularized articulated registration process, in which geometrical
model fitting is combined with statistical, kinematic and temporal regularization
priors. A registration concept that combines 2D and 3D alignment and explicitly accounts
for occlusions and visibility constraints is devised. High-quality, non-invasive,
real-time hand tracking is achieved based on this regularized articulated registration
formulation
- …