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Proceedings ArticleDOI

Human motion analysis: a review

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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.

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Patent

Compound gesture-speech commands

TL;DR: In this paper, a multimedia entertainment system combines both gestures and voice commands to provide an enhanced control scheme, where a user's body position or motion may be recognized as a gesture, and may be used to provide context to recognize user generated sounds, such as speech input.
Proceedings ArticleDOI

Automated gesture segmentation from dance sequences

TL;DR: An algorithm called hierarchical activity segmentation is proposed, which employs a dynamic hierarchical layered structure to represent human anatomy, and uses low-level motion parameters to characterize motion in the various layers of this hierarchy, which correspond to different segments of the human body.
Journal ArticleDOI

Efficient duration and hierarchical modeling for human activity recognition

TL;DR: This paper exploits efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and applies it to the problem of learning and recognizing ADLs with complex temporal dependencies, demonstrating that Coxian modeling outperforms a range of baseline models for the task of activity segmentation.
Journal ArticleDOI

Observation and analysis of large-scale human motion

TL;DR: The baseline of the approach consists of sacrificing much of the spatial accuracy and temporal resolution of widely used biomechanical measurement systems, to obtain data on human movement that span large areas and long intervals of time.
Patent

Moving Object Segmentation Using Depth Images

TL;DR: In this article, a moving object is segmented from the background of a depth image of a scene received from a mobile depth camera using an iterative closest point algorithm, which includes a determination of a set of points that correspond between the current depth image and the previous depth image.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

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Journal ArticleDOI

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