[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kitsikidis et al., 2015 - Google Patents

Unsupervised dance motion patterns classification from fused skeletal data using exemplar-based HMMs

Kitsikidis et al., 2015

View PDF
Document ID
4865641770923908139
Author
Kitsikidis A
Boulgouris N
Dimitropoulos K
Grammalidis N
Publication year
Publication venue
International Journal of Heritage in the Digital Era

External Links

Snippet

In this paper, we propose a method for the partitioning of dance sequences into multiple periods and motion patterns. The proposed method deploys features in the form of a skeletal representation of the dancer observed through time using multiple depth sensors. This …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • G06K9/00355—Recognition of hand or arm movements, e.g. recognition of deaf sign language
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • G06K9/00369—Recognition of whole body, e.g. static pedestrian or occupant recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions

Similar Documents

Publication Publication Date Title
Zhang et al. Martial arts, dancing and sports dataset: A challenging stereo and multi-view dataset for 3d human pose estimation
Zheng et al. Hybridfusion: Real-time performance capture using a single depth sensor and sparse imus
Baak et al. A data-driven approach for real-time full body pose reconstruction from a depth camera
Ganapathi et al. Real-time human pose tracking from range data
Schwarz et al. Human skeleton tracking from depth data using geodesic distances and optical flow
Ji et al. Advances in view-invariant human motion analysis: A review
Lee et al. Human pose tracking in monocular sequence using multilevel structured models
Del Rincón et al. Tracking human position and lower body parts using Kalman and particle filters constrained by human biomechanics
US8639020B1 (en) Method and system for modeling subjects from a depth map
US9058663B2 (en) Modeling human-human interactions for monocular 3D pose estimation
Chang et al. The model-based human body motion analysis system
Atrevi et al. A very simple framework for 3D human poses estimation using a single 2D image: Comparison of geometric moments descriptors
Yan et al. Cimi4d: A large multimodal climbing motion dataset under human-scene interactions
Schwarz et al. Manifold learning for tof-based human body tracking and activity recognition.
Vasileiadis et al. Robust human pose tracking for realistic service robot applications
Bang et al. Camera pose estimation using optical flow and ORB descriptor in SLAM-based mobile AR game
Gall et al. Drift-free tracking of rigid and articulated objects
Kitsikidis et al. Unsupervised dance motion patterns classification from fused skeletal data using exemplar-based HMMs
Güdükbay et al. Motion capture and human pose reconstruction from a single-view video sequence
Correa et al. Silhouette-based probabilistic 2D human motion estimation for real-time applications
Huo et al. Multiple people tracking and pose estimation with occlusion estimation
Cheng et al. Capturing human motion in natural environments
Brauer et al. On the effect of temporal information on monocular 3d human pose estimation
Bullock et al. Towards real-time 3-D monocular visual tracking of human limbs in unconstrained environments
Hernandez et al. Bayesian approach for morphology-based 2-D human motion capture