7,889 research outputs found
Discovery and recognition of motion primitives in human activities
We present a novel framework for the automatic discovery and recognition of
motion primitives in videos of human activities. Given the 3D pose of a human
in a video, human motion primitives are discovered by optimizing the `motion
flux', a quantity which captures the motion variation of a group of skeletal
joints. A normalization of the primitives is proposed in order to make them
invariant with respect to a subject anatomical variations and data sampling
rate. The discovered primitives are unknown and unlabeled and are
unsupervisedly collected into classes via a hierarchical non-parametric Bayes
mixture model. Once classes are determined and labeled they are further
analyzed for establishing models for recognizing discovered primitives. Each
primitive model is defined by a set of learned parameters.
Given new video data and given the estimated pose of the subject appearing on
the video, the motion is segmented into primitives, which are recognized with a
probability given according to the parameters of the learned models.
Using our framework we build a publicly available dataset of human motion
primitives, using sequences taken from well-known motion capture datasets. We
expect that our framework, by providing an objective way for discovering and
categorizing human motion, will be a useful tool in numerous research fields
including video analysis, human inspired motion generation, learning by
demonstration, intuitive human-robot interaction, and human behavior analysis
Human Perambulation as a Self Calibrating Biometric
This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios
Gait recognition based on shape and motion analysis of silhouette contours
This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods
Gait Recognition from Motion Capture Data
Gait recognition from motion capture data, as a pattern classification
discipline, can be improved by the use of machine learning. This paper
contributes to the state-of-the-art with a statistical approach for extracting
robust gait features directly from raw data by a modification of Linear
Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU
MoCap database show that the suggested method outperforms thirteen relevant
methods based on geometric features and a method to learn the features by a
combination of Principal Component Analysis and Linear Discriminant Analysis.
The methods are evaluated in terms of the distribution of biometric templates
in respective feature spaces expressed in a number of class separability
coefficients and classification metrics. Results also indicate a high
portability of learned features, that means, we can learn what aspects of walk
people generally differ in and extract those as general gait features.
Recognizing people without needing group-specific features is convenient as
particular people might not always provide annotated learning data. As a
contribution to reproducible research, our evaluation framework and database
have been made publicly available. This research makes motion capture
technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia
Computing, Communications, and Applications (TOMM), special issue on
Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin
note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392,
arXiv:1609.0693
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Noninvasive Neuroprosthetic Control of Grasping by Amputees
Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control.Electrical and Computer Engineering, Department o
Human-like arm motion generation: a review
In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
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