369 research outputs found
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
Analysis of the hands in egocentric vision: A survey
Egocentric vision (a.k.a. first-person vision - FPV) applications have
thrived over the past few years, thanks to the availability of affordable
wearable cameras and large annotated datasets. The position of the wearable
camera (usually mounted on the head) allows recording exactly what the camera
wearers have in front of them, in particular hands and manipulated objects.
This intrinsic advantage enables the study of the hands from multiple
perspectives: localizing hands and their parts within the images; understanding
what actions and activities the hands are involved in; and developing
human-computer interfaces that rely on hand gestures. In this survey, we review
the literature that focuses on the hands using egocentric vision, categorizing
the existing approaches into: localization (where are the hands or parts of
them?); interpretation (what are the hands doing?); and application (e.g.,
systems that used egocentric hand cues for solving a specific problem).
Moreover, a list of the most prominent datasets with hand-based annotations is
provided
3D human action recognition in multiple view scenarios
This paper presents a novel view-independent
approach to the recognition of human gestures of several
people in low resolution sequences from multiple calibrated
cameras. In contraposition with other multi-ocular gesture
recognition systems based on generating a classification on
a fusion of features coming from different views, our system
performs a data fusion (3D representation of the scene) and
then a feature extraction and classification. Motion descriptors
introduced by Bobick et al. for 2D data are extended
to 3D and a set of features based on 3D invariant statistical
moments are computed. Finally, a Bayesian classifier is employed
to perform recognition over a small set of actions. Results
are provided showing the effectiveness of the proposed
algorithm in a SmartRoom scenario.Peer ReviewedPostprint (published version
3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review
Three-dimensional (3D) point cloud analysis has become one of the attractive
subjects in realistic imaging and machine visions due to its simplicity,
flexibility and powerful capacity of visualization. Actually, the
representation of scenes and buildings using 3D shapes and formats leveraged
many applications among which automatic driving, scenes and objects
reconstruction, etc. Nevertheless, working with this emerging type of data has
been a challenging task for objects representation, scenes recognition,
segmentation, and reconstruction. In this regard, a significant effort has
recently been devoted to developing novel strategies, using different
techniques such as deep learning models. To that end, we present in this paper
a comprehensive review of existing tasks on 3D point cloud: a well-defined
taxonomy of existing techniques is performed based on the nature of the adopted
algorithms, application scenarios, and main objectives. Various tasks performed
on 3D point could data are investigated, including objects and scenes
detection, recognition, segmentation and reconstruction. In addition, we
introduce a list of used datasets, we discuss respective evaluation metrics and
we compare the performance of existing solutions to better inform the
state-of-the-art and identify their limitations and strengths. Lastly, we
elaborate on current challenges facing the subject of technology and future
trends attracting considerable interest, which could be a starting point for
upcoming research studie
Articulated motion and deformable objects
This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field
Reconnaissance de postures humaines par fusion de la silhouette et de l'ombre dans l'infrarouge
Les systèmes multicaméras utilisés pour la vidéosurveillance sont complexes, lourds et coûteux. Pour la surveillance d'une pièce, serait-il possible de les remplacer par un système beaucoup plus simple utilisant une seule caméra et une ou plusieurs sources lumineuses en misant sur les ombres projetées pour obtenir de l'information 3D ?
Malgré les résultats intéressants offerts par les systèmes multicaméras, la quantité d'information à traiter et leur complexité limitent grandement leur usage. Dans le même contexte, nous proposons de simplifier ces systèmes en remplaçant une caméra par une source lumineuse. En effet, une source lumineuse peut être vue comme une caméra qui génère une image d'ombre révélant l'objet qui bloque la lumière. Notre système sera composé par une seule caméra et une ou plusieurs sources lumineuses infrarouges (invisibles à l'oeil). Malgré les difficultés prévues quant à l'extraction de l'ombre et la déformation et l'occultation de l'ombre par des obstacles (murs, meubles...), les gains sont multiples en utilisant notre système. En effet, on peut éviter ainsi les problèmes de synchronisation et de calibrage de caméras et réduire le coût en remplaçant des caméras par de simples sources infrarouges.
Nous proposons deux approches différentes pour automatiser la reconnaissance de postures humaines. La première approche reconstruit la forme 3D d'une personne pour faire la reconnaissance de la posture en utilisant des descripteurs de forme. La deuxième approche combine directement l'information 2D (ombre+silhouette) pour faire la reconnaissance de postures.
Scientifiquement, nous cherchons à prouver que l'information offerte par une silhouette et l'ombre générée par une source lumineuse est suffisante pour permettre la reconnaissance de postures humaines élémentaires (p.ex. debout, assise, couchée, penchée, etc.).
Le système proposé peut être utilisé pour la vidéosurveillance d'endroits non encombrés tels qu'un corridor dans une résidence de personnes âgées (pour la détection des chutes p. ex.) ou d'une compagnie (pour la sécurité). Son faible coût permettrait un plus grand usage de la vidéosurveillance au bénéfice de la société. Au niveau scientifique, la démonstration théorique et pratique d'un tel système est originale et offre un grand potentiel pour la vidéosurveillance.Human posture recognition (HPR) from video sequences is one of the major active
research areas of computer vision. It is one step of the global process of human activity
recognition (HAR) for behaviors analysis. Many HPR application systems have
been developed including video surveillance, human-machine interaction, and the video
retrieval. Generally, applications related to HPR can be achieved using mainly two
approaches : single camera or multi-cameras. Despite the interesting performance achieved
by multi-camera systems, their complexity and the huge information to be processed
greatly limit their widespread use for HPR.
The main goal of this thesis is to simplify the multi-camera system by replacing a
camera by a light source. In fact, a light source can be seen as a virtual camera, which
generates a cast shadow image representing the silhouette of the person that blocks the
light. Our system will consist of a single camera and one or more infrared light sources.
Despite some technical difficulties in cast shadow segmentation and cast shadow deformation
because of walls and furniture, different advantages can be achieved by using our
system. Indeed, we can avoid the synchronization and calibration problems of multiple
cameras, reducing the cost of the system and the amount of processed data by replacing
a camera by one light source.
We introduce two different approaches in order to automatically recognize human
postures. The first approach directly combines the person’s silhouette and cast shadow
information, and uses 2D silhouette descriptor in order to extract discriminative features
useful for HPR. The second approach is inspired from the shape from silhouette technique
to reconstruct the visual hull of the posture using a set of cast shadow silhouettes,
and extract informative features through 3D shape descriptor. Using these approaches,
our goal is to prove the utility of the combination of person’s silhouette and cast shadow
information for recognizing elementary human postures (stand, bend, crouch, fall,...)
The proposed system can be used for video surveillance of uncluttered areas such as
a corridor in a senior’s residence (for example, for the detection of falls) or in a company (for security). Its low cost may allow greater use of video surveillance for the benefit of
society
HAND GESTURE RECOGNITION SEBAGAI ALAT INTERAKSI DAN OPERASI KOMPUTER MENGGUNAKAN ALGORITMA CONVEX-HULL
In today's era, technological developments are very rapid, including developments in computer peripheral devices, one of which is the mouse. However, the mouse still has several weaknesses, namely the need for a fairly broad media to use it, whereas now on computers, there are applications such as interactive games and Augmented Reality (AR) that require more flexible interaction, the game industry is getting bigger day by day, as well as many variations. As games and playing patterns are increasingly widespread, hand gesture recognition is one of the computer operating solutions that needs to be developed to meet these game patterns, so it is necessary to develop an easier and more intuitive computer operating method. So the interaction between humans and computing devices can be achieved if hand gesture recognition can be used for communication between humans and computing devices. Hand gesture recognition will be more useful considering that in the current era with the Covid-19 pandemic, we also have to keep ourselves away from the chain of virus transmission that can spread more easily when we use hardware in public facilities in turn. The application of artificial intelligence technology as well as machine learning in this study found that after the recognition test the light intensity level of 202,3 lm/m2 was the most optimal condition for this gesture recognition application to run effectively and efficiently.
Keywords: Hand Gesture, Recognition, Machine Learning, Covid-19, Convex-hul
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