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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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Citations
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Journal ArticleDOI

Feature-level combination of skeleton joints and body parts for accurate aggressive and agitated behavior recognition

TL;DR: The proposed approach is validated using extensive experiments on variety of challenging 3D action datasets for human behavior recognition and empirically demonstrate that it accurately discriminates between behaviors and performs better than several state of the art algorithms.
Book ChapterDOI

Multi-camera tracking of articulated human motion using motion and shape cues

TL;DR: A method for fusing information from different spatial cues such as silhouettes and “motion residues” into a single energy function is presented, and the optimum pose for which the energy is minimised is found.
Journal ArticleDOI

Gait-based human age classification using a silhouette model

TL;DR: A gait-based descriptor for age classification using a silhouette projection model that aims to represent the arms' swing, the head's pitch, the hunched posture and the stride's length, which are among the most outstanding ageing characteristics that appear on the elderly's gait.
Patent

Systems And Methods For Detecting A Tilt Angle From A Depth Image

TL;DR: In this paper, a human target in the depth image was scanned for one or more body parts such as shoulders, hips, knees, or the like, and a tilt angle was calculated based on the body parts.
Journal ArticleDOI

Knowledge Acquisition Method Based on Singular Value Decomposition for Human Motion Analysis

TL;DR: A new method using singular value decomposition for extracting embodied knowledge from the time-series data of the motion and using the left singular vectors of the matrix as the patterns of themotion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix.
References
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Proceedings ArticleDOI

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Pfinder: real-time tracking of the human body

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Proceedings ArticleDOI

Recognizing human action in time-sequential images using hidden Markov model

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Journal ArticleDOI

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TL;DR: The author uses projective relations as the theoretical foundation of his investigations of visual space and motion and concludes that during locomotion the components of the human visual environment are interpreted as rigid structures in relative motion.
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