Proceedings ArticleDOI
Human motion analysis: a review
Jake K. Aggarwal,Qin Cai +1 more
- pp 90-102
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TLDR
The paper gives an overview of the various tasks involved in motion analysis of the human body, and focuses on three major areas related to interpreting human motion: motion analysis involving human body parts, tracking of human motion using single or multiple cameras, and recognizing human activities from image sequences.Abstract:
Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. The paper gives an overview of the various tasks involved in motion analysis of the human body. The authors focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion using single or multiple cameras, and 3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion using a single or multiple camera focuses on higher-level processing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image from one frame to another in image sequences, understanding the human movements or activities comes naturally, which leads to a discussion of recognizing human activities. The review is illustrated by examples.read more
Citations
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Speech recognition analysis via identification information
Jason Flaks,Dax Hawkins,Christian Klein,Mitchell Stephen Dernis,Tommer Leyvand,Ali M. Vassigh,Duncan McKay +6 more
TL;DR: In this article, the use of identity information to help avoid the occurrence of false positive speech recognition events in a speech recognition system is discussed, where the acoustic locational data is compared to the visual locational information to determine whether the recognized speech segment originated from a person in the field of view of the image sensor, and confidence data is adjusted depending on this determination.
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Isolate Extraneous Motions
TL;DR: In this article, a system may receive image data and capture motion with respect to a target in a physical space and recognize a gesture from the captured motion by isolating aspects of captured motion to differentiate random and extraneous motions.
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The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria
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TL;DR: In this paper, the authors proposed the Contracting Curve Density (CCD) algorithm, which applies a novel likelihood function for the assessment of a fit between the curve model and the image data.
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Systems and methods for removing a background of an image
TL;DR: In this article, a grid of voxels is generated based on the depth image such that a depth image may be downsampled, and a background is discarded to isolate one or more voxel associated with a foreground object such as a human target.
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